WO2023286247A1 - Inspection device, inspection system, inspection method, and inspection program - Google Patents

Inspection device, inspection system, inspection method, and inspection program Download PDF

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Publication number
WO2023286247A1
WO2023286247A1 PCT/JP2021/026640 JP2021026640W WO2023286247A1 WO 2023286247 A1 WO2023286247 A1 WO 2023286247A1 JP 2021026640 W JP2021026640 W JP 2021026640W WO 2023286247 A1 WO2023286247 A1 WO 2023286247A1
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Prior art keywords
inspection
vibration
objects
measurement data
frequency
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PCT/JP2021/026640
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French (fr)
Japanese (ja)
Inventor
貢汰 貞本
宏 荒木
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三菱電機株式会社
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Priority to PCT/JP2021/026640 priority Critical patent/WO2023286247A1/en
Priority to JP2023534548A priority patent/JPWO2023286247A1/ja
Publication of WO2023286247A1 publication Critical patent/WO2023286247A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/02—Vibration-testing by means of a shake table

Definitions

  • the present disclosure relates to an inspection device, an inspection system, an inspection method, and an inspection program.
  • Patent Document 1 measures the vibration intensity of a vibrated inspection object and analyzes the measurement data to determine whether or not there is a structural abnormality such as a crack in the inspection object (that is, the presence or absence of structural abnormality ) is disclosed.
  • This device measures the vibration intensity for each frequency of the object to be vibrated and detects the frequencies of a plurality of vibration peaks. A correlation coefficient between the frequencies of the vibration peaks is calculated, and the presence or absence of structural abnormalities in the inspection target is diagnosed based on this correlation coefficient.
  • the above-described conventional apparatus diagnoses the presence or absence of an abnormality using a plurality of vibration peaks of the reference object and a plurality of vibration peaks of the inspection object. There is a problem that the accuracy of diagnosing the presence or absence of a structural abnormality deteriorates when noise components are included in the measured vibration intensity.
  • the purpose of the present disclosure is to provide an inspection device, an inspection system, an inspection method, and an inspection program that make it possible to inspect the presence or absence of an abnormality in an inspection target with high diagnostic accuracy.
  • the inspection apparatus of the present disclosure detects N (N is an integer of 3 or more) as the object from the vibration detection unit that detects the vibration of the object vibrated by the vibrating unit and outputs the measured value of the vibration.
  • a data collection unit that collects the measured values for each frequency of the vibration as measurement data for each of the inspection objects; and a similarity of the measurement data for each combination of two inspection objects among the N inspection objects.
  • a data analysis unit that calculates the degree of similarity and calculates an evaluation value that is a statistic of the similarity for each of the N inspection objects; and a data analysis unit that compares the evaluation values of each of the N inspection objects, and a diagnosis unit for diagnosing whether or not each of the N inspection objects has an abnormality based on the result of the comparison.
  • the inspection method of the present disclosure detects N (N is an integer of 3 or more) as the object from the vibration detection unit that detects the vibration of the object vibrated by the vibrating unit and outputs the measured value of the vibration.
  • the presence or absence of abnormality in the inspection target can be inspected with high diagnostic accuracy.
  • FIG. 1 is a schematic diagram showing configurations of an inspection apparatus and an inspection system according to Embodiment 1;
  • FIG. 4A to 4D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 1 as frequency/vibration intensity characteristics;
  • FIG. 4 is a diagram showing processing performed by a data analysis unit of the inspection apparatus according to Embodiment 1;
  • FIG. 4 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG.
  • FIG. 10 is a schematic diagram showing the configuration of an inspection apparatus and an inspection system according to Embodiment 2; (A) and (B) are diagrams showing a vibration mode and a vibration detector of an object to be inspected which is excited by a vibration exciter of the inspection system according to Embodiment 2.
  • FIG. FIG. 8 is a diagram showing the relationship between the vibration exciter of the inspection system according to Embodiment 2 and the vibration modes shown in FIGS.
  • FIG. 8(A) and 8(B); 8A to 8D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 2 as frequency/vibration intensity characteristics;
  • FIG. 10 is a diagram showing processing performed by a data analysis unit of the inspection device according to Embodiment 2;
  • FIG. 12 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 11 and the frequency;
  • FIG. 10 is a diagram showing processing performed by a data analysis unit of the inspection device according to Embodiment 2
  • FIG. 12 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 11 and the frequency
  • FIG. 11 is a schematic diagram showing configurations of an inspection apparatus and an inspection system according to Embodiment 4;
  • A) and (B) are diagrams showing vibration modes and vibration detectors of an object to be inspected which are excited by a vibration exciter of an inspection system according to Embodiment 4.
  • FIG. 17A and 17B are diagrams showing the relationship between the vibration detector of the inspection system according to Embodiment 4 and the vibration modes shown in FIGS. 17A and 17B;
  • FIGS. 11A to 11D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 4 as frequency/vibration intensity characteristics;
  • FIG. FIG. 13 is a diagram showing processing performed by a data analysis unit of an inspection apparatus according to Embodiment 4;
  • FIG. 21 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 20 and the frequency;
  • FIG. 11 is a schematic diagram showing the configuration of an inspection apparatus and an inspection system according to Embodiment 5; It is a figure which shows the example of the hardware configuration of the inspection apparatus and inspection system which concern on Embodiment 1-5.
  • FIG. 5 is a diagram showing another example of the hardware configuration of the inspection apparatus and inspection system according to Embodiments 1 to 5;
  • FIG. 1 is a schematic diagram showing configurations of an inspection apparatus 11 and an inspection system 1 according to the first embodiment.
  • the inspection system 1 has a vibrator 21 , a vibration detector 31 and an inspection device 11 .
  • the inspection device 11 has a data collection section 41 , a data analysis section 51 and a diagnosis section 61 .
  • the inspection apparatus 11 is an apparatus capable of implementing the inspection method according to the first embodiment.
  • the inspection apparatus 11 inspects whether or not an inspection target 100 as an object has an abnormality (that is, whether or not there is an abnormality).
  • the inspection for the presence/absence of abnormality is mainly an inspection for the presence/absence of structural abnormality.
  • Structural anomalies include, for example, cracks or fissures (i.e., cracks) in the test object 100, missing portions of the test object 100, deformation of the test object 100, and incomplete installation of parts that make up the test object 100. , etc.
  • the vibrating section 21 has a vibrator 21a.
  • the vibrator 21a is composed of, for example, a piezoelectric element capable of vibrating the inspection target 100 at a frequency of 1 kHz or more.
  • the vibration exciter 21a vibrates the inspection object 100 by contacting the inspection object 100, for example.
  • the vibration detector 31 has a vibration detector 31a.
  • the vibration detection unit 31 is composed of, for example, a piezoelectric element or the like capable of detecting vibration of the inspection object 100 at a frequency of 1 kHz or higher.
  • the vibration detector 31 a measures the vibration of the inspection object 100 by contacting the inspection object 100 .
  • the data collection unit 41 detects N (N is an integer of 3 or more) inspections of objects from the vibration detection unit 31 that detects the vibration of the object vibrated by the vibrating unit 21 and outputs the vibration measurement value. For each object, measured values for each vibration frequency are collected as measurement data. The measurement data is also called a measurement result. The data collection unit 41 collects measurement data regarding the vibration intensity detected by the vibration detection unit 31 .
  • the data analysis unit 51 performs a process of calculating the correlation coefficient of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects. Then, an evaluation value, which is the sum or average of the correlation coefficients, is calculated for each of the N test objects. In Embodiment 1, the data analysis unit 51 calculates an average of correlation coefficients with measurement data of N inspection objects.
  • the diagnosis unit 61 compares the evaluation values of the N inspection objects with each other, and diagnoses whether or not each of the N inspection objects has an abnormality (for example, structural abnormality) based on the comparison result. Presence/absence diagnosis). That is, the diagnosis unit 61 compares and analyzes the sum or average of the correlation coefficients calculated by the data analysis unit 51 for each test object, diagnoses the presence or absence of abnormality for each test object, and outputs the result.
  • an abnormality for example, structural abnormality
  • the inspection device 11 is configured by, for example, a processing circuit.
  • the processing circuit may be configured by a processor such as a CPU (Central Processing Unit) that executes a program as software stored in a memory.
  • the processor may be a processing circuit such as a system LSI. Also, multiple processors and multiple memories may work together to achieve the above functions.
  • the number of inspection objects may be three or more, but in the first embodiment, an example in which the number of inspection objects is four will be described as an example.
  • Each inspection object is a part (that is, an article) manufactured with the same design, and is denoted as inspection object #1, inspection object #2, inspection object #3, and inspection object #4.
  • Inspection objects #1 to #3 are normal products (for example, products with no structural abnormality), and inspection object #4 is an abnormal product (for example, products with structural abnormality).
  • An abnormal product is, for example, an object to be inspected in which cracks of the order of several hundred ⁇ m to several mm or more have occurred due to aged deterioration or the like.
  • the vibrating section 21 vibrates each test object in a predetermined frequency band of 1 kHz or higher.
  • the positions to be vibrated are not particularly limited. However, if there is a vibrating position where the difference in the correlation coefficient between the normal product and the abnormal product remarkably appears depending on the inspection target, it is desirable to vibrate the position.
  • the vibration detection unit 31 detects the vibration intensity when each inspection object is vibrated.
  • the data collection unit 41 collects the vibration intensity detected by the vibration detection unit 31 as measurement data.
  • FIGS. 2A to 2D show examples of measurement data 201 to 204 of inspection objects #1 to #4 collected by the data collection unit 41 of the inspection apparatus 11 according to the first embodiment. It is a figure shown as. As shown in FIGS. 2A to 2C, the measurement data 201 to 203 of inspection objects #1 to #3, which are normal products, show generally the same tendency. On the other hand, as shown in FIG. 2(D), the measurement data 204 of the inspection object #4, which is an abnormal product, has a vibration peak frequency higher than that of the measurement data 201 to 203 of the inspection objects #1 to #3. Or the number of vibration peaks is different.
  • the data analysis unit 51 analyzes the measurement data 201 to 204 collected by the data collection unit 41.
  • the data analysis unit 51 calculates the correlation coefficient between each of the measurement data 201 to 204 of the inspection objects #1 to #4 and the measurement data of the other inspection objects, and calculates the average of the correlation coefficients.
  • Pearson's product-moment correlation coefficient shown in the following equation (1) is used as the correlation coefficient r.
  • a and B represent two inspection targets, and A i and B i are measurement data collected by the data collection unit 41 at frequency i, respectively.
  • the frequency range is an arbitrary range in the frequency band of measurement data.
  • a ave is the average value of A i in the above frequency range
  • B ave is the average value of B i in the above frequency range.
  • FIG. 3 is a diagram showing processing performed by the data analysis unit 51 of the inspection device 11 according to the first embodiment.
  • the data analysis unit 51 calculates the correlation coefficients of the measurement data 202 to 204 of the inspection objects #2 to #4 with respect to the measurement data 201 of the inspection object #1. 0.98 and 0.60. Also, the average is 0.86.
  • the data analysis unit 51 performs the same processing for inspection objects other than inspection object #1.
  • the average correlation coefficient for inspection objects #1 to #3, which are normal products, is in the range of 0.83 to 0.86, while the average correlation coefficient for inspection object #4, which is an abnormal product, is 0. 0.59, which is significantly different from the average of the correlation coefficients for the inspection objects #1 to #3.
  • FIG. 4 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 3 and the frequency.
  • the diagnosis unit 61 diagnoses the presence or absence of abnormality in the inspection object based on the analysis result of the data analysis unit 51, and outputs the diagnosis result.
  • the width of the distribution is set to 0.1. Note that the width of the distribution may be another value.
  • the average of the correlation coefficients for inspection objects #1 to #3, which are normal products, belongs to the frequency 402 of 0.8 to 0.9.
  • the average correlation coefficient for inspection object #4 which is an abnormal product, belongs to the frequency 401 of 0.5 to 0.6.
  • the average correlation coefficient for inspection object #4 which is an abnormal product, is an outlier. Therefore, it is possible to detect the average of test object #4 as an outlier by a clustering technique such as the mean shift method or hierarchical clustering. Inspection target #4 detected as an outlier is determined to be abnormal. On the other hand, since the inspection objects #1 to #3 are distributed in the same positions, they are determined as normal products. The above result is output as a diagnosis result of the presence or absence of abnormality. Note that when all the inspection objects are normal products, the frequencies are distributed at substantially the same position, so they are classified into the same class by clustering, and all the inspection objects are determined as normal products.
  • FIG. 5 is a flow chart showing the operation of the inspection device 11 according to the first embodiment.
  • the data collection unit 41 detects N (N is an integer of 3 or more) inspections of objects from the vibration detection unit 31 that detects the vibration of the object vibrated by the vibrating unit 21 and outputs the vibration measurement value. Measured values for each vibration frequency are collected as measurement data for each object (step S1).
  • the data analysis unit 51 calculates the correlation coefficient of the measurement data for each combination of two inspection objects out of the N inspection objects, and calculates the sum or average of the correlation coefficients for each of the N inspection objects. A certain evaluation value is calculated (step S2).
  • the diagnosis unit 61 compares the evaluation values of the N inspection objects with each other, and diagnoses whether or not each of the N inspection objects has an abnormality based on the comparison result (step S3).
  • the average of the correlation coefficients for normal inspection objects becomes large.
  • the average correlation coefficient for abnormal test objects is small. Therefore, even if there is no object that has been confirmed to be normal in advance, it is possible to diagnose the presence or absence of an abnormality by determining the presence or absence of an outlier from the average of the correlation coefficients for each inspection object.
  • the frequency of the vibration of the inspection target detected by the vibration detection unit 31 is set to a predetermined frequency band of 1 kHz or more. In this case, the following first to third effects are obtained.
  • the first effect is that detecting vibrations of several kHz or higher makes it possible to detect cracks on the order of several hundred ⁇ m to several mm. This is described, for example, in Non-Patent Document 1 below.
  • the second effect is that if the frequency band in which the response change due to cracking is large is known in advance, the diagnostic accuracy can be improved by detecting vibration in the frequency band of several kHz or higher.
  • a third effect is that the vibration-to-noise ratio can be improved by not detecting frequencies below 1 kHz, which are not well suited for detecting cracks.
  • the inspection apparatus or inspection method according to Embodiment 1 can be used to inspect parts installed in a narrow space of a machine, such as a technique for inspecting a rotor wedge of a generator. It has the following fourth to sixth effects compared with the technique described in Non-Patent Document 2, which diagnoses the presence or absence of an abnormality.
  • the fourth effect will be explained.
  • the ultrasonic flaw detection it is necessary to measure all the positions of the parts to be inspected, and there is a problem that the inspection time is long.
  • the inspection apparatus or inspection method according to Embodiment 1 it is possible to inspect each part, so inspection time can be shortened.
  • the inspection apparatus or inspection method according to Embodiment 1 can use a piezoelectric element, and the thickness of the piezoelectric element is several millimeters or less, so it can be applied to a larger number of inspection objects.
  • FIG. 6 is a diagram showing an example in which the vibrating section 21 and the vibration detecting section 31 of the inspection system 1 according to the first embodiment are mounted on the moving body 71 .
  • the inspection system 1 may be one in which the vibrating section 21 and the vibration detecting section 31 are mounted on a mobile body 71 such as a mobile robot, and measurements can be performed remotely.
  • a mobile body 71 such as a mobile robot
  • the gap through which a moving object can be inserted is about 100 mm or less. Therefore, by setting the thickness of the vibrating portion 21 and the vibration detecting portion 31 to 100 mm or less, it is possible to realize a configuration that allows inspection of the rotor wedge.
  • the data analysis unit 51 may use the average of the correlation coefficients calculated in each of a plurality of predetermined frequency ranges as the correlation coefficient with the measurement data of each inspection target. If there is a vibration mode with a significantly higher vibration intensity than other vibration modes, the correlation coefficient is greatly affected by this vibration mode, and response changes occur depending on the presence or absence of abnormalities in other vibration modes. There is also the problem that it is less likely to be affected by Therefore, by calculating the correlation coefficient for each of a plurality of frequency bands, it is possible to calculate normalized correlation coefficients in each of the frequency band in which the vibration intensity of the vibration mode is large and the band in which the vibration intensity is small. Therefore, it is possible to calculate a correlation coefficient that equalizes the influence regardless of the vibration intensity of each vibration mode.
  • the information detected by the vibration detection unit 31 is not the vibration intensity, but may be phase information, such as the difference between the phase of the signal that vibrates the test object by the vibration excitation unit 21 and the phase of the vibration detected by the vibration detection unit 31. good. Further, the information detected by the vibration detection unit 31 is information obtained by combining the vibration intensity and the phase of the signal for vibrating the inspection object by the vibrating unit 21 or the phase difference of the vibration detected by the vibration detection unit 31. good too.
  • the data analysis unit 51 performs the process of calculating the correlation coefficient of the measurement data of two inspection objects out of the N inspection objects for all two inspection objects out of the N inspection objects. Instead, it may be executed for each object to be inspected.
  • the data analysis unit 51 may calculate other degrees of similarity instead of the correlation coefficient.
  • the data analysis unit 51 may use the Euclidean distance calculated by using the vibration intensity of each frequency of the measurement data 201 to 204 as the length of each dimension. Similarity is a correlation coefficient or Euclidean distance.
  • the data analysis unit 51 may calculate other statistics such as the sum, the median, and the trimmed average instead of the similarity average.
  • the data analysis unit 51 performs the process of calculating the similarity of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects. is executed, and an evaluation value, which is a similarity statistic, is calculated for each of the N inspection targets.
  • diagnosis unit 61 may diagnose whether there is an abnormality by threshold determination instead of clustering.
  • the vibration exciter 21a may be configured such that it strikes an object to be inspected such as a hammer, and the vibration detector 31a may be configured to detect the vibration of a microphone or the like and measure the vibration intensity of each frequency.
  • an electrodynamic vibrator or the like may be used as the vibrator 21a.
  • an acceleration sensor or the like may be used as the vibration detector 31a.
  • the vibrator 21a may vibrate the object to be inspected in a non-contact manner using a laser or the like, instead of vibrating the object to be inspected by contact. Further, the vibration detector 31a does not need to be in contact to detect the vibration of the object to be inspected.
  • FIG. 7 is a schematic diagram showing configurations of an inspection apparatus 12 and an inspection system 2 according to the second embodiment.
  • the inspection system 2 has a vibrator 22 , a vibration detector 31 and an inspection device 12 .
  • the inspection device 12 has a data collection section 42 , a data analysis section 52 and a diagnosis section 62 .
  • the inspection device 12 is a device capable of implementing the inspection method according to the second embodiment.
  • the inspection device 12 diagnoses the presence or absence of an abnormality in the inspection object.
  • the vibrating section 22 has a plurality of vibrators.
  • Embodiment 2 describes a case where the vibrating section 22 has two vibrators 22a and 22b.
  • the two vibrators 22 a and 22 b vibrate different positions of the inspection target 100 .
  • the vibration detection section 31 in the second embodiment is the same as that described in the first embodiment.
  • the data collection unit 42 collects measurement data related to the vibration intensity detected by the vibration detection unit 31 when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. .
  • the data analysis unit 52 analyzes the measurement data of the three or more inspection objects measured when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. , and the average of the correlation coefficients is calculated.
  • the diagnosis unit 62 calculates the average of the correlation coefficients of the inspection objects calculated by the data analysis unit 52 when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. are compared and analyzed, the presence or absence of abnormality is diagnosed for each inspection object, and the result is output.
  • FIGS. 8A and 8B show vibration modes 301 and 302 of an object to be inspected excited by the vibrators 22a and 22b of the vibrating unit 22 of the inspection system 2 according to the second embodiment, and the vibration detector 31a. It is a figure which shows .
  • the vibrators 22a and 22b of the vibrating section 22 vibrate in a predetermined frequency band of 1 kHz or higher. While one vibrator vibrates the test object, the other vibrator does not vibrate the test object.
  • FIG. 9 is a diagram showing the relationship between the vibration exciters 22a and 22b of the inspection system 2 according to Embodiment 2 and the vibration modes 301 and 320 shown in FIGS. 8(A) and 8(B).
  • Vibration mode 301 can be excited by vibrator 22a whose installation location is not a node of vibration mode 301, while it can be excited by vibration exciter 22b whose installation location is a node of vibration mode 301. Can not.
  • the vibration mode 302 cannot be excited by the vibration exciter 22a whose installation location is a node of the vibration mode 302, but can be excited by the vibration exciter 22b whose installation location is not a node of the vibration mode 302. can.
  • FIG. 9 shows vibration modes that can be excited by each of the vibration exciters 22a and 22b (indicated by circle marks “ ⁇ ”) and vibration modes that cannot be excited (indicated by cross marks “X”). are shown in tabular form.
  • Vibrational mode 301 can be excited by shaker 22a and vibrational mode 302 can be excited by shaker 22b, using both shakers to excite both vibration modes 301, 302. can do.
  • the vibration detection unit 31 detects the vibration intensity when each test object is vibrated when the vibrator 22a vibrates the test object and when the vibrator 22b vibrates the test object.
  • FIGS. 10A to 10D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit 42 of the inspection apparatus 12 according to the second embodiment as frequency/vibration intensity characteristics.
  • the data collection unit 42 collects measurement data related to the vibration intensity detected by the vibration detection unit 31 when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. .
  • the data collection unit 42 acquires measurement data 201 to 204 shown in FIGS. 2(A) to 2(D).
  • the vibrator 22b vibrates the object to be inspected
  • the data collection unit 42 acquires the measurement data 205 to 208 shown in FIGS. 10(A) to 10(D).
  • the measurement data 208 of the inspection object #4, which is an abnormal product differs from the measurement data 205 to 208 of the inspection objects #1 to #3 in the frequency of vibration peaks or the number of vibration peaks.
  • the vibration modes that can be vibrated are different between the vibrators 22a and 22b, so that the measurement data are different. Since cracks have different effects on vibration modes depending on their state, there are cases where they do not affect one vibration mode, but greatly affect the other vibration mode. Therefore, by collecting measurement data obtained by applying vibrations by both the vibrators 22a and 22b, it becomes easier to acquire measurement data in which the responses of the normal product and the abnormal product are significantly different.
  • FIG. 11 is a diagram showing processing performed by the data analysis unit 52 of the inspection device 12 according to the second embodiment.
  • the data analysis unit 52 performs analysis in the same manner as in the first embodiment when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target.
  • the average correlation coefficient for the test objects #1 to #3 is in the range of 0.83 to 0.86.
  • the average correlation coefficient for test object #4 is 0.59.
  • the analysis result when the vibration exciter 22b vibrates the object to be inspected is as shown in FIG.
  • the average correlation coefficient for measurement data #1 to #3 is in the range of 0.80 to 0.81, while the average correlation coefficient for test object #4 is 0.48. significantly different from the average correlation coefficient for ⁇ #3.
  • FIG. 12 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 11 and the frequency.
  • the diagnosis unit 62 diagnoses the presence or absence of an abnormality based on the analysis result of the data analysis unit 52 when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. , and outputs its diagnostic results.
  • the determination result of the presence/absence of an abnormality when the vibration exciter 22a vibrates the object to be inspected is as described in the first embodiment.
  • FIG. 12 shows the average distribution of the correlation coefficients for the inspection objects #1 to #4 when the vibration exciter 22b vibrates the inspection object.
  • the average correlation coefficient for inspection object #4, which is an abnormal product belongs to the frequency 403 of 0.4 to 0.5.
  • Inspection object #4 is determined to be abnormal by a clustering method as in the first embodiment. It should be noted that when the vibration exciter 22b vibrates the inspection object, the average difference in the correlation coefficients between the inspection objects #1 to #3, which are normal products, and the inspection object #4, which is an abnormal product, is larger. can be confirmed. In other words, the accuracy of diagnosing the presence or absence of an abnormality is higher when the vibration exciter 22b vibrates the object to be inspected. As described above, the diagnosis accuracy can be improved by using a plurality of vibrators.
  • the inspection apparatus 12 and inspection method according to the second embodiment are suitable for use when there is little noise.
  • the vibration exciter 22b vibrates the inspection target the difference in the correlation coefficient between the normal product and the abnormal product becomes larger than when the vibration exciter 22a vibrates the inspection target. Presence or absence can be determined.
  • FIG. 13 is a schematic diagram showing the configuration of the inspection apparatus 13 and the inspection system 3 according to the third embodiment.
  • the inspection system 3 has a vibrator 22 , a vibration detector 31 and an inspection device 13 .
  • the inspection device 13 has a data collection section 43 , a data analysis section 53 and a diagnosis section 63 .
  • the inspection device 13 is a device capable of implementing the inspection method according to the third embodiment.
  • the inspection device 13 diagnoses the presence or absence of abnormality in the inspection object.
  • the vibrating section 22 and the vibration detecting section 31 are the same as those in the second embodiment.
  • the data analysis unit 53 calculates the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency in a plurality of measurement data measured for the same inspection object. do.
  • Diagnosis section 63 is the same as diagnosis section 61 in the first embodiment.
  • the number of inspection objects may be three or more, an example in which four inspection objects #1 to #4 are inspected will be described in the third embodiment.
  • the vibrators 22a and 22b of the vibrating section 22 vibrate in a predetermined frequency band of 1 kHz or more as in the case of the second embodiment.
  • the vibration detection unit 31 vibrates each inspection target when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. Detects the vibration intensity when shaken.
  • the data collection unit 43 detects that the vibration detection unit 31 is Collect measurement data on the detected vibration intensity.
  • the data analysis unit 53 calculates the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency in a plurality of measurement data measured for the same inspection object.
  • 14A to 14D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 3 as frequency/vibration intensity characteristics. .
  • the measurement data 201 to 204 of the inspection objects #1 to #4 are the measurement data collected by the data collection unit 43 when the vibration exciter 22a vibrates the inspection object.
  • the measurement data 205 to 208 of the inspection objects #1 to #4 are the measurement data collected by the data collection unit 43 when the vibration exciter 22b vibrates the inspection object. While the vibration intensity of each frequency of both detects a similar vibration peak, it may detect a different vibration peak.
  • 15A to 15D show the maximum values of the vibration intensity of the measurement data 209 to 212 of the inspection objects #1 to #4 collected by the data collection unit 43 of the inspection apparatus 13 according to the third embodiment. - It is a figure shown as a vibration strength characteristic. As a result, it is possible to detect the vibration peak detected by both measurement data, and capture the vibration peak whose response changes depending on the abnormal portion. Next, the average of the correlation coefficients for each frequency/vibration intensity characteristic is calculated using the same method as in the first embodiment.
  • the diagnosis unit 63 diagnoses whether there is an abnormality based on the analysis result of the data analysis unit 53, and outputs the diagnosis result.
  • FIG. 16 is a schematic diagram showing configurations of an inspection apparatus 14 and an inspection system 4 according to the fourth embodiment.
  • the inspection system 4 has a vibrator 21 , a vibration detector 32 and an inspection device 14 .
  • the inspection device 14 has a data collection section 44 , a data analysis section 54 and a diagnosis section 64 .
  • the inspection device 14 is a device capable of implementing the inspection method according to the fourth embodiment.
  • the inspection device 14 diagnoses the presence or absence of abnormality in the inspection object.
  • the inspection device 14 is composed of a vibrating section 21, a vibration detection section 32, a data collection section 44, a data analysis section 54, and a diagnosis section 64, and diagnoses the presence or absence of abnormality in the inspection target.
  • the vibrating section 21 is the same as that described in the first embodiment.
  • the vibration detector 32 has a plurality of vibration detectors. In Embodiment 4, the vibration detector 32 has vibration detectors 32a and 32b.
  • the data collection unit 44 collects measurement data related to vibration intensity when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration.
  • the data analysis unit 54 determines the measurement data of the three or more test targets for each of the measurement data of the other test targets. Calculate the correlation coefficient with the data, and calculate the average of the correlation coefficients.
  • the diagnosis unit 64 compares and analyzes the average correlation coefficients calculated by the data analysis unit 54 for each test object when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration. Then, the presence or absence of abnormality is diagnosed for each inspection object, and the result is output.
  • the number of inspection objects may be three or more.
  • the vibrator 21a of the vibrating section 21 vibrates in a predetermined frequency band of 1 kHz or higher, as in the case of the first embodiment.
  • FIGS. 17A and 17B are diagrams showing the vibration modes of the inspection object excited by the vibration exciter 21a of the inspection system 4 according to Embodiment 4 and the vibration detectors 32a and 32b.
  • Each of the vibration detectors 32a and 32b of the vibration detection unit 32 detects vibration intensity when each inspection object is vibrated.
  • the vibration mode 301 can be detected by the vibration detector 32a whose installation location is not the node of the vibration mode 301, but cannot be detected by the vibration detector 32b whose installation location is the node of the vibration mode 301.
  • the vibration mode 302 cannot be detected by the vibration detector 32a whose installation location is the node of the vibration mode 302, but can be detected by the vibration detector 32b whose installation location is not the node of the vibration mode 302. can.
  • FIG. 18 is a diagram showing the relationship between the vibration detector of the inspection system according to Embodiment 4 and the vibration modes shown in FIGS. 17(A) and 17(B). Vibration modes 301 and 302 can be detected only by vibration detectors 32a and 32b, respectively, and both vibration modes 301 and 302 can be detected by using both vibration detectors 32a and 32b.
  • FIGS. 19A to 19D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit 44 of the inspection apparatus according to the fourth embodiment as frequency/vibration intensity characteristics. be.
  • the data collection unit 44 collects measurement data regarding vibration intensity when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration.
  • measurement data 201 to 204 shown in FIGS. 2(A) to 2(D) are obtained.
  • measurement data 213 to 216 shown in FIGS. 19A to 19D are obtained.
  • the measurement data 216 of the inspection object #4, which is an abnormal product differs from the measurement data 213 to 215 of the inspection objects #1 to #3 in the frequency of vibration peaks or the number of vibration peaks.
  • the vibration detectors 32a and 32b detects vibration, the two detectable vibration modes are different, so the measurement data are different. Cracks have different effects on vibration modes depending on the state of the cracks. Therefore, there are cases where cracks do not affect a certain vibration mode, but greatly affect the other vibration mode. Therefore, by collecting the measurement data detected by both the vibration detectors 32a and 32b, it becomes easier to acquire the measurement data whose responses are significantly different between the normal product and the abnormal product.
  • FIG. 20 is a diagram showing processing performed by the data analysis unit 54 of the inspection device 14 according to the fourth embodiment.
  • the data analysis unit 54 performs analysis in the same manner as in the first embodiment when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration.
  • the average correlation coefficient for the inspection objects #1 to #3 is in the range of 0.83 to 0.86.
  • the average correlation coefficient for test object #4 is 0.59.
  • FIG. 20 shows the analysis result when the vibration detector 32b detects vibration.
  • the average correlation coefficient for the measurement data #1 to #3 is 0.81, while the average correlation coefficient for the test object #4 is 0.48. Very different from the number average.
  • FIG. 21 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 20 and the frequency.
  • the diagnosis unit 64 diagnoses the presence or absence of an abnormality based on the analysis results of the data analysis unit 54 when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration, and the diagnosis result is to output The determination result of the presence or absence of abnormality when the vibration detector 32a detects vibration is as described in the first embodiment.
  • the average correlation coefficients for inspection objects #1 to #3 which are normal products, belong to the frequency 406 of 0.8 to 0.9.
  • the average correlation coefficient for inspection object #4 which is an abnormal product, belongs to the frequency 405 of 0.4 to 0.5.
  • Inspection object #4 is determined to be abnormal by a clustering method as in the first embodiment. It should be noted that when the vibration detector 32b detects vibration, the average difference in the correlation coefficients between the inspection objects #1 to #3, which are normal products, and the inspection object #4, which is an abnormal product, is larger. I can confirm. That is, when the vibration detector 32b detects vibration, the accuracy of diagnosing the presence or absence of abnormality is higher. As described above, by using a plurality of vibration detectors, the accuracy of diagnosis can be improved.
  • the inspection apparatus 14 and the inspection method according to the fourth embodiment in addition to the effects described in the first embodiment, by using a plurality of vibration detectors, the sensitivity to abnormalities is higher. By collecting measurement data, it becomes easier to obtain results with different correlation coefficients between normal products and abnormal products, so that diagnostic accuracy can be improved.
  • An example has been described in which the presence or absence of an abnormality can be correctly diagnosed in both cases where the vibration detectors 32a and 32b detect vibration. It may get worse. Therefore, the inspection apparatus 14 and inspection method according to the fourth embodiment are suitable for use when there is little noise.
  • the data analysis unit 54 uses the measurement data detected by the vibration detectors 32a and 32b to measure a plurality of measurements of the same inspection target.
  • the presence or absence of abnormality may be diagnosed by a method of calculating the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency of the data.
  • the diagnosis unit 64 diagnoses whether there is an abnormality based on the analysis result of the data analysis unit 54 and outputs the diagnosis result, as in the first embodiment.
  • FIG. 22 is a schematic diagram showing configurations of an inspection apparatus 15 and an inspection system 5 according to the fifth embodiment.
  • the inspection system 5 has a vibrator 21 , a vibration detector 31 and an inspection device 15 .
  • the inspection device 15 has a data collection section 45 , a data analysis section 55 and a diagnosis section 65 .
  • the inspection device 15 is a device capable of implementing the inspection method according to the fifth embodiment.
  • the inspection device 15 diagnoses the presence or absence of abnormality in the inspection object.
  • the vibration exciter 21a of the vibration excitation unit 21 and the vibration detector 31a of the vibration detection unit 31 are installed on a transport line that transports the inspection objects 101 to 106 in the transport direction.
  • the inspection device 15 has a data collection unit 45, a data analysis unit 55, and a diagnosis unit 65, and diagnoses the presence or absence of abnormality in the inspection object.
  • the vibrating section 21, the vibration detecting section 31, and the data collecting section 45 are the same as those described in the first embodiment.
  • the data analysis unit 55 calculates the correlation coefficient of a predetermined number of test objects close to the current time, that is, the predetermined number of test objects collected most recently, among the measurement data collected by the data collection unit 45. Execute the process of calculating the evaluation value of .
  • the data analysis unit 55 analyzes the measurement data of the four inspection targets from which data has been collected most recently, in the same manner as in the first embodiment.
  • Diagnosis unit 65 diagnoses the presence or absence of abnormalities in the four most recently collected test objects based on the analysis results of data analysis unit 55 in the same manner as in the first embodiment, and outputs the diagnosis results. .
  • the vibrating section 21 vibrates each test object flowing on the carrier line in a predetermined frequency band of 1 kHz or more, as in the case of the first embodiment.
  • the vibration detection unit 31 detects the vibration intensity of each inspection object flowing on the transfer line when each inspection object is vibrated, as in the case of the first embodiment.
  • the data collection unit 45 collects measurement data related to the vibration intensity detected by the vibration detection unit 31, as in the case of the first embodiment.
  • the data analysis unit 55 analyzes the measurement data of the four most recently collected inspection targets in the same manner as in the first embodiment. Therefore, when the measurement data regarding the inspection object 104 is collected, the most recently obtained measurement data regarding the inspection objects 101 to 104 are analyzed.
  • Diagnosis unit 65 diagnoses the presence or absence of abnormalities in the recently collected four inspection objects based on the analysis result of data analysis unit 55, as in the case of the first embodiment, and performs the diagnosis. Print the result.
  • diagnosis of the presence or absence of an abnormality is performed using the four most recently collected inspection objects.
  • FIG. 23 is a diagram showing an example of the hardware configuration of the inspection apparatuses 11 to 15 and the inspection systems 1 to 5 according to the first to fifth embodiments.
  • the inspection apparatuses 11 to 15 according to Embodiments 1 to 5 have a processor 501 such as a CPU, a memory 502 as a storage device, and an interface 503 .
  • the vibrating section 21 (or 22) and the vibration detecting section 31 (or 32) are connected to the interface 503.
  • the processor 501 executes programs as software stored in the memory 502 (for example, inspection programs according to Embodiments 1 to 5).
  • the inspection devices 11-15 may be computers.
  • FIG. 24 is a diagram showing another example of the hardware configuration of the inspection apparatuses 11-15 and inspection systems 1-5 according to Embodiments 1-5.
  • the inspection apparatuses 11 to 15 according to Embodiments 1 to 5 have a processing circuit 504 and an interface 503.
  • the vibrating section 21 (or 22) and the vibration detecting section 31 (or 32) are connected to the interface 503.
  • the processing circuit 504 is, for example, a semiconductor integrated circuit, a system LSI, or a field-programmable gate array (FPGA) that constitutes the inspection apparatuses 11 to 15 according to the first to fifth embodiments.
  • one or more processors, one or more memories, and processing circuits may cooperate to implement the functions of the inspection devices 11-15.
  • 1 to 5 inspection system 11 to 15 inspection device, 21, 22 vibration part, 21a, 22a, 22b vibration exciter, 31, 32 vibration detection part, 31a, 32a, 32b vibration detector, 41 to 45 data collection part , 51 to 55 Data analysis part, 61 to 65 Diagnosis part, 71 Moving body, 100 to 106 Inspection object, 201 to 216 Measurement data, 301, 302 Amplitude of vibration mode, 401 to 406 Average distribution frequency of correlation coefficient .

Abstract

An inspection device (11) comprises: a data collection unit (41) that collects, as measurement data, measurement values for each vibration frequency for each of N (where N is an integer greater than or equal to 3) objects under inspection from a vibration detection unit (31) that outputs vibration measurement values by detecting vibration in the objects, which are vibrated by a vibration unit (21); a data analysis unit (51) that, for each combination of two objects under inspection from among the N objects under inspection, carries out processing for calculating the similarity of the measurement data for the two objects under inspection from among the N objects under inspection, and calculates evaluation values that are similarity statistics for each of the N objects under inspection; and a diagnosis unit (61) that compares the evaluation values for the N objects under inspection with each other and uses the result of the comparison to produce a diagnosis for each of the N objects under inspection as to whether the same is abnormal.

Description

検査装置、検査システム、検査方法、及び検査プログラムInspection device, inspection system, inspection method, and inspection program
　本開示は、検査装置、検査システム、検査方法、及び検査プログラムに関する。 The present disclosure relates to an inspection device, an inspection system, an inspection method, and an inspection program.
　特許文献１は、加振された検査対象の振動強度を計測し、計測データを解析することで、検査対象にひび割れ等の構造上の異常が有るか無いか（すなわち、構造上の異常の有無）を診断する装置を開示している。この装置は、加振された検査対象の周波数ごとの振動強度を計測して複数の振動ピークの周波数を検出し、検出された周波数と、正常と確認された基準対象物について予め検出した複数の振動ピークの周波数との間の相関係数を算出し、この相関係数に基づいて検査対象の構造上の異常の有無を診断する。 Patent Document 1 measures the vibration intensity of a vibrated inspection object and analyzes the measurement data to determine whether or not there is a structural abnormality such as a crack in the inspection object (that is, the presence or absence of structural abnormality ) is disclosed. This device measures the vibration intensity for each frequency of the object to be vibrated and detects the frequencies of a plurality of vibration peaks. A correlation coefficient between the frequencies of the vibration peaks is calculated, and the presence or absence of structural abnormalities in the inspection target is diagnosed based on this correlation coefficient.
特開２００８－２３２７６３号公報JP-A-2008-232763
　しかしながら、上記従来の装置は、基準対象物の複数の振動ピークと検査対象の複数の振動ピークとを用いて異常の有無を診断しているので、複数の振動ピークの周波数の間隔が狭い場合又は計測された振動強度に雑音成分が含まれている場合に、構造上の異常の有無の診断精度が悪化するという問題がある。 However, the above-described conventional apparatus diagnoses the presence or absence of an abnormality using a plurality of vibration peaks of the reference object and a plurality of vibration peaks of the inspection object. There is a problem that the accuracy of diagnosing the presence or absence of a structural abnormality deteriorates when noise components are included in the measured vibration intensity.
　本開示は、検査対象の異常の有無を高い診断精度で検査することを可能にする検査装置、検査システム、検査方法、及び検査プログラムを提供することを目的とする。 The purpose of the present disclosure is to provide an inspection device, an inspection system, an inspection method, and an inspection program that make it possible to inspect the presence or absence of an abnormality in an inspection target with high diagnostic accuracy.
　本開示の検査装置は、加振部によって加振された物体の振動を検出して前記振動の計測値を出力する振動検出部から、前記物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、前記振動の周波数ごとの前記計測値を計測データとして収集するデータ収集部と、前記Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について前記計測データの類似度を算出し、前記Ｎ個の検査対象の各々について前記類似度の統計量である評価値を算出するデータ解析部と、前記Ｎ個の検査対象の各々の前記評価値を互いに比較し、前記比較の結果に基づいて前記Ｎ個の検査対象の各々に異常が有るか無いかを診断する診断部と、を有することを特徴とする。 The inspection apparatus of the present disclosure detects N (N is an integer of 3 or more) as the object from the vibration detection unit that detects the vibration of the object vibrated by the vibrating unit and outputs the measured value of the vibration. A data collection unit that collects the measured values for each frequency of the vibration as measurement data for each of the inspection objects; and a similarity of the measurement data for each combination of two inspection objects among the N inspection objects. a data analysis unit that calculates the degree of similarity and calculates an evaluation value that is a statistic of the similarity for each of the N inspection objects; and a data analysis unit that compares the evaluation values of each of the N inspection objects, and a diagnosis unit for diagnosing whether or not each of the N inspection objects has an abnormality based on the result of the comparison.
　本開示の検査方法は、加振部によって加振された物体の振動を検出して前記振動の計測値を出力する振動検出部から、前記物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、前記振動の周波数ごとの前記計測値を計測データとして収集するステップと、前記Ｎ個の検査対象のうちの２個の検査対象の前記計測データの類似度を算出する処理を、前記Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、前記Ｎ個の検査対象の各々について前記類似度の統計量である評価値を算出するステップと、前記Ｎ個の検査対象の各々の前記評価値を互いに比較し、前記比較の結果に基づいて前記Ｎ個の検査対象の各々に異常が有るか無いかの診断を行うステップとを有することを特徴とする。 The inspection method of the present disclosure detects N (N is an integer of 3 or more) as the object from the vibration detection unit that detects the vibration of the object vibrated by the vibrating unit and outputs the measured value of the vibration. A step of collecting the measurement values for each frequency of the vibration as measurement data for each inspection object, and a process of calculating the similarity of the measurement data of two inspection objects among the N inspection objects. , executing for each combination of two test objects out of the N test objects, and calculating an evaluation value that is a statistic of the similarity for each of the N test objects; comparing the evaluation values of each of the N inspection objects with each other, and diagnosing whether or not each of the N inspection objects has an abnormality based on the result of the comparison. .
　本開示によれば、検査対象の異常の有無を高い診断精度で検査することができる。 According to the present disclosure, the presence or absence of abnormality in the inspection target can be inspected with high diagnostic accuracy.
実施の形態１に係る検査装置及び検査システムの構成を示す概略図である。1 is a schematic diagram showing configurations of an inspection apparatus and an inspection system according to Embodiment 1; FIG. （Ａ）から（Ｄ）は、実施の形態１に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。4A to 4D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 1 as frequency/vibration intensity characteristics; FIG. 実施の形態１に係る検査装置のデータ解析部によって行われる処理を示す図である。4 is a diagram showing processing performed by a data analysis unit of the inspection apparatus according to Embodiment 1; FIG. 図３に示される相関係数の平均と度数との関係を示す度数分布図である。FIG. 4 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 3 and the frequency; 実施の形態１に係る検査装置の動作を示すフローチャートである。4 is a flow chart showing the operation of the inspection apparatus according to Embodiment 1; 実施の形態１に係る検査システムの加振部と振動検出部とを移動体に搭載した例を示す図である。4 is a diagram showing an example in which the vibrating section and the vibration detecting section of the inspection system according to Embodiment 1 are mounted on a moving object; FIG. 実施の形態２に係る検査装置及び検査システムの構成を示す概略図である。FIG. 10 is a schematic diagram showing the configuration of an inspection apparatus and an inspection system according to Embodiment 2; （Ａ）及び（Ｂ）は、実施の形態２に係る検査システムの加振器によって加振された検査対象の振動モードと振動検出器とを示す図である。(A) and (B) are diagrams showing a vibration mode and a vibration detector of an object to be inspected which is excited by a vibration exciter of the inspection system according to Embodiment 2. FIG. 実施の形態２に係る検査システムの加振器と図８（Ａ）及び（Ｂ）に示される振動モードとの関係を示す図である。FIG. 8 is a diagram showing the relationship between the vibration exciter of the inspection system according to Embodiment 2 and the vibration modes shown in FIGS. 8(A) and 8(B); （Ａ）から（Ｄ）は、実施の形態２に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。8A to 8D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 2 as frequency/vibration intensity characteristics; FIG. 実施の形態２に係る検査装置のデータ解析部によって行われる処理を示す図である。FIG. 10 is a diagram showing processing performed by a data analysis unit of the inspection device according to Embodiment 2; 図１１に示される相関係数の平均と度数との関係を示す度数分布図である。FIG. 12 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 11 and the frequency; 実施の形態３に係る検査装置及び検査システムの構成を示す概略図である。FIG. 11 is a schematic diagram showing the configuration of an inspection apparatus and an inspection system according to Embodiment 3; （Ａ）から（Ｄ）は、実施の形態３に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。8A to 8D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 3 as frequency/vibration intensity characteristics; FIG. （Ａ）から（Ｄ）は、実施の形態３に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの振動強度の最大値を周波数・振動強度特性として示す図である。(A) to (D) are diagrams showing, as frequency/vibration intensity characteristics, maximum values of vibration intensity of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 3; is. 実施の形態４に係る検査装置及び検査システムの構成を示す概略図である。FIG. 11 is a schematic diagram showing configurations of an inspection apparatus and an inspection system according to Embodiment 4; （Ａ）及び（Ｂ）は、実施の形態４に係る検査システムの加振器によって加振された検査対象の振動モードと振動検出器とを示す図である。(A) and (B) are diagrams showing vibration modes and vibration detectors of an object to be inspected which are excited by a vibration exciter of an inspection system according to Embodiment 4. FIG. 実施の形態４に係る検査システムの振動検出器と図１７（Ａ）及び（Ｂ）に示される振動モードとの関係を示す図である。17A and 17B are diagrams showing the relationship between the vibration detector of the inspection system according to Embodiment 4 and the vibration modes shown in FIGS. 17A and 17B; FIG. （Ａ）から（Ｄ）は、実施の形態４に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。11A to 11D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 4 as frequency/vibration intensity characteristics; FIG. 実施の形態４に係る検査装置のデータ解析部によって行われる処理を示す図である。FIG. 13 is a diagram showing processing performed by a data analysis unit of an inspection apparatus according to Embodiment 4; 図２０に示される相関係数の平均と度数との関係を示す度数分布図である。FIG. 21 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 20 and the frequency; 実施の形態５に係る検査装置及び検査システムの構成を示す概略図である。FIG. 11 is a schematic diagram showing the configuration of an inspection apparatus and an inspection system according to Embodiment 5; 実施の形態１から５に係る検査装置及び検査システムのハードウェア構成の例を示す図である。It is a figure which shows the example of the hardware configuration of the inspection apparatus and inspection system which concern on Embodiment 1-5. 実施の形態１から５に係る検査装置及び検査システムのハードウェア構成の他の例を示す図である。FIG. 5 is a diagram showing another example of the hardware configuration of the inspection apparatus and inspection system according to Embodiments 1 to 5;
　以下に、実施の形態に係る検査装置、検査システム、検査方法、及び検査プログラムを、図面を参照しながら説明する。以下の実施の形態は、例にすぎず、実施の形態を適宜組み合わせること及び各実施の形態を適宜変更することが可能である。なお、各図において、同じ又は同様の構成には、同じ符号が付されている。 The inspection device, inspection system, inspection method, and inspection program according to the embodiment will be described below with reference to the drawings. The following embodiments are merely examples, and the embodiments can be combined as appropriate and each embodiment can be modified as appropriate. In addition, in each figure, the same code|symbol is attached|subjected to the same or similar structure.
《１》実施の形態１
《１－１》構成
　図１は、実施の形態１に係る検査装置１１及び検査システム１の構成を示す概略図である。図１に示されるように、検査システム１は、加振部２１と、振動検出部３１と、検査装置１１とを有している。検査装置１１は、データ収集部４１と、データ解析部５１と、診断部６１とを有している。検査装置１１は、実施の形態１に係る検査方法を実施することができる装置である。検査装置１１は、物体としての検査対象１００に異常が有るか無いか（すなわち、異常の有無）の検査を実行する。異常の有無の検査は、主に、構造上の異常の有無の検査である。構造上の異常は、例えば、検査対象１００の亀裂又はひび割れ（すなわち、クラック）、検査対象１００の一部の欠損、検査対象１００の変形、及び、検査対象１００を構成する部品の不完全な取り付け、などである。 <<1>> Embodiment 1
<<1-1>> Configuration FIG. 1 is a schematic diagram showing configurations of an inspection apparatus 11 and an inspection system 1 according to the first embodiment. As shown in FIG. 1 , the inspection system 1 has a vibrator 21 , a vibration detector 31 and an inspection device 11 . The inspection device 11 has a data collection section 41 , a data analysis section 51 and a diagnosis section 61 . The inspection apparatus 11 is an apparatus capable of implementing the inspection method according to the first embodiment. The inspection apparatus 11 inspects whether or not an inspection target 100 as an object has an abnormality (that is, whether or not there is an abnormality). The inspection for the presence/absence of abnormality is mainly an inspection for the presence/absence of structural abnormality. Structural anomalies include, for example, cracks or fissures (i.e., cracks) in the test object 100, missing portions of the test object 100, deformation of the test object 100, and incomplete installation of parts that make up the test object 100. , etc.
　加振部２１は、加振器２１ａを有している。加振器２１ａは、例えば、検査対象１００を１ｋＨｚ以上の周波数で加振することができる圧電素子などで構成される。加振器２１ａは、例えば、検査対象１００に接触して検査対象１００を加振する。 The vibrating section 21 has a vibrator 21a. The vibrator 21a is composed of, for example, a piezoelectric element capable of vibrating the inspection target 100 at a frequency of 1 kHz or more. The vibration exciter 21a vibrates the inspection object 100 by contacting the inspection object 100, for example.
　振動検出部３１は、振動検出器３１ａを有している。振動検出部３１は、例えば、検査対象１００の１ｋＨｚ以上の周波数の振動を検出することができる圧電素子などで構成される。振動検出器３１ａは、検査対象１００に接触して検査対象１００の振動を計測する。 The vibration detector 31 has a vibration detector 31a. The vibration detection unit 31 is composed of, for example, a piezoelectric element or the like capable of detecting vibration of the inspection object 100 at a frequency of 1 kHz or higher. The vibration detector 31 a measures the vibration of the inspection object 100 by contacting the inspection object 100 .
　データ収集部４１は、加振部２１によって加振された物体の振動を検出して振動の計測値を出力する振動検出部３１から、物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、振動の周波数ごとの計測値を計測データとして収集する。計測データは、計測結果ともいう。データ収集部４１は、振動検出部３１が検出した振動強度に関する計測データを収集する。 The data collection unit 41 detects N (N is an integer of 3 or more) inspections of objects from the vibration detection unit 31 that detects the vibration of the object vibrated by the vibrating unit 21 and outputs the vibration measurement value. For each object, measured values for each vibration frequency are collected as measurement data. The measurement data is also called a measurement result. The data collection unit 41 collects measurement data regarding the vibration intensity detected by the vibration detection unit 31 .
　データ解析部５１は、Ｎ個の検査対象のうちの２個の検査対象の計測データの相関係数を算出する処理を、Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、Ｎ個の検査対象の各々について相関係数の総和又は平均である評価値を算出する。実施の形態１では、データ解析部５１は、Ｎ個の検査対象の計測データとの相関係数の平均を算出する。 The data analysis unit 51 performs a process of calculating the correlation coefficient of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects. Then, an evaluation value, which is the sum or average of the correlation coefficients, is calculated for each of the N test objects. In Embodiment 1, the data analysis unit 51 calculates an average of correlation coefficients with measurement data of N inspection objects.
　診断部６１は、Ｎ個の検査対象の各々の評価値を互いに比較し、比較の結果に基づいてＮ個の検査対象の各々に異常が有るか無いかの診断（例えば、構造上の異常の有無の診断）を行う。つまり、診断部６１は、データ解析部５１が算出した各検査対象における相関係数の総和又は平均を比較分析して、検査対象ごとの異常の有無を診断し、その結果を出力する。 The diagnosis unit 61 compares the evaluation values of the N inspection objects with each other, and diagnoses whether or not each of the N inspection objects has an abnormality (for example, structural abnormality) based on the comparison result. Presence/absence diagnosis). That is, the diagnosis unit 61 compares and analyzes the sum or average of the correlation coefficients calculated by the data analysis unit 51 for each test object, diagnoses the presence or absence of abnormality for each test object, and outputs the result.
　検査装置１１は、例えば、処理回路によって構成される。処理回路は、メモリに記憶されたソフトウェアとしてのプログラムを実行するＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）などのプロセッサなどにより構成されてもよい。プロセッサは、システムＬＳＩなどの処理回路であってもよい。また、複数のプロセッサ、及び複数のメモリが連携して上記機能を実現してもよい。 The inspection device 11 is configured by, for example, a processing circuit. The processing circuit may be configured by a processor such as a CPU (Central Processing Unit) that executes a program as software stored in a memory. The processor may be a processing circuit such as a system LSI. Also, multiple processors and multiple memories may work together to achieve the above functions.
《１－２》動作
　検査対象は、３個以上であればよいが、実施の形態１では、一例として検査対象が４個であるときの例について説明する。各検査対象は、同じ設計で製作された部品（すなわち、物品）であり、検査対象＃１、検査対象＃２、検査対象＃３、検査対象＃４と表記される。検査対象＃１～＃３は、正常品（例えば、構造上の異常が無い商品）であり、検査対象＃４が異常品（例えば、構造上の異常が有る商品）である。異常品は、例えば、経年劣化等によって、数百μｍから数ｍｍオーダ以上のひび割れが発生した検査対象である。 <<1-2>> Operation The number of inspection objects may be three or more, but in the first embodiment, an example in which the number of inspection objects is four will be described as an example. Each inspection object is a part (that is, an article) manufactured with the same design, and is denoted as inspection object #1, inspection object #2, inspection object #3, and inspection object #4. Inspection objects #1 to #3 are normal products (for example, products with no structural abnormality), and inspection object #4 is an abnormal product (for example, products with structural abnormality). An abnormal product is, for example, an object to be inspected in which cracks of the order of several hundred μm to several mm or more have occurred due to aged deterioration or the like.
　加振部２１は、各検査対象を１ｋＨｚ以上において予め定められた周波数帯域で加振する。検査対象＃１～＃４に対して同じ位置であれば、加振する位置は特に限定されない。ただし、検査対象によって、正常品と異常品との間で相関係数の差が顕著に表れる加振位置があれば、その位置に加振することが望ましい。 The vibrating section 21 vibrates each test object in a predetermined frequency band of 1 kHz or higher. As long as the positions are the same for the inspection objects #1 to #4, the positions to be vibrated are not particularly limited. However, if there is a vibrating position where the difference in the correlation coefficient between the normal product and the abnormal product remarkably appears depending on the inspection target, it is desirable to vibrate the position.
　振動検出部３１は、各検査対象を加振したときの振動強度を検出する。データ収集部４１は、振動検出部３１が検出した振動強度を計測データとして収集する。 The vibration detection unit 31 detects the vibration intensity when each inspection object is vibrated. The data collection unit 41 collects the vibration intensity detected by the vibration detection unit 31 as measurement data.
　図２（Ａ）から（Ｄ）は、実施の形態１に係る検査装置１１のデータ収集部４１によって収集された検査対象＃１～＃４の計測データ２０１～２０４の例を周波数・振動強度特性として示す図である。図２（Ａ）から（Ｃ）に示されるように、正常品である検査対象＃１～＃３の計測データ２０１～２０３は、概ね同じ傾向を示している。一方で、図２（Ｄ）に示されるように、異常品である検査対象＃４の計測データ２０４は、検査対象＃１～＃３の計測データ２０１～２０３と比較して、振動ピークの周波数又は振動ピークの数が異なっている。 FIGS. 2A to 2D show examples of measurement data 201 to 204 of inspection objects #1 to #4 collected by the data collection unit 41 of the inspection apparatus 11 according to the first embodiment. It is a figure shown as. As shown in FIGS. 2A to 2C, the measurement data 201 to 203 of inspection objects #1 to #3, which are normal products, show generally the same tendency. On the other hand, as shown in FIG. 2(D), the measurement data 204 of the inspection object #4, which is an abnormal product, has a vibration peak frequency higher than that of the measurement data 201 to 203 of the inspection objects #1 to #3. Or the number of vibration peaks is different.
　データ解析部５１は、データ収集部４１が収集した計測データ２０１～２０４の解析を行う。データ解析部５１は、検査対象＃１～＃４の計測データ２０１～２０４のそれぞれに関して、他の検査対象の計測データとの相関係数を算出し、その相関係数の平均を算出する。実施の形態では、相関係数ｒとして、以下の式（１）に示される、ピアソンの積率相関係数が用いられている。 The data analysis unit 51 analyzes the measurement data 201 to 204 collected by the data collection unit 41. The data analysis unit 51 calculates the correlation coefficient between each of the measurement data 201 to 204 of the inspection objects #1 to #4 and the measurement data of the other inspection objects, and calculates the average of the correlation coefficients. In the embodiment, Pearson's product-moment correlation coefficient shown in the following equation (1) is used as the correlation coefficient r.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　式（１）において、Ａ、Ｂは、２個の検査対象を表し、Ａｉ、Ｂｉは、それぞれ周波数ｉにおけるデータ収集部４１が収集した計測データである。周波数の範囲は、計測データの周波数帯域において任意の範囲である。また、Ａａｖｅは、上記周波数の範囲におけるＡｉの平均値であり、Ｂａｖｅは、上記周波数の範囲におけるＢｉの平均値である。 In Equation (1), A and B represent two inspection targets, and A i and B i are measurement data collected by the data collection unit 41 at frequency i, respectively. The frequency range is an arbitrary range in the frequency band of measurement data. Also, A ave is the average value of A i in the above frequency range, and B ave is the average value of B i in the above frequency range.
　図３は、実施の形態１に係る検査装置１１のデータ解析部５１によって行われる処理を示す図である。データ解析部５１は、検査対象＃１の計測データ２０１に対する、検査対象＃２～＃４の計測データ２０２～２０４の相関係数を算出し、算出された相関係数は、それぞれ０．９９、０．９８、０．６０となる。また、その平均は、０．８６となる。データ解析部５１は、検査対象＃１以外の検査対象についても同じ処理を行う。正常品である検査対象＃１～＃３に関する相関係数の平均は、０．８３～０．８６の範囲にある一方で、異常品である検査対象＃４に関する相関係数の平均は、０．５９となり、検査対象＃１～＃３に関する相関係数の平均と大きく異なる。 FIG. 3 is a diagram showing processing performed by the data analysis unit 51 of the inspection device 11 according to the first embodiment. The data analysis unit 51 calculates the correlation coefficients of the measurement data 202 to 204 of the inspection objects #2 to #4 with respect to the measurement data 201 of the inspection object #1. 0.98 and 0.60. Also, the average is 0.86. The data analysis unit 51 performs the same processing for inspection objects other than inspection object #1. The average correlation coefficient for inspection objects #1 to #3, which are normal products, is in the range of 0.83 to 0.86, while the average correlation coefficient for inspection object #4, which is an abnormal product, is 0. 0.59, which is significantly different from the average of the correlation coefficients for the inspection objects #1 to #3.
　図４は、図３に示される相関係数の平均と度数との関係を示す度数分布図である。診断部６１は、データ解析部５１の解析結果を元に検査対象の異常の有無を診断し、その診断結果を出力する。図４においては、分布の幅を０．１としている。なお、分布の幅は、他の値であってもよい。正常品である検査対象＃１～＃３に関する相関係数の平均は、０．８～０．９の度数４０２に属している。一方で、異常品である検査対象＃４に関する相関係数の平均は、０．５～０．６の度数４０１に属している。検査対象＃１～＃４に関する相関係数の平均の分布において、異常品である検査対象＃４に関する相関係数の平均が外れ値となっている。したがって、ミーンシフト法又は階層的クラスタリングなどのクラスタリングに関する手法などによって検査対象＃４の平均を外れ値として検出することが可能である。外れ値として検出した検査対象＃４は、異常と判定される。一方で、検査対象＃１～＃３は、同様の位置に分布しているため、正常品として判定する。以上の結果を異常の有無の診断結果として出力する。なお、全ての検査対象が正常品である場合は、全て度数がほぼ同じ位置に分布するため、クラスタリングによって同一のクラスとなり、全ての検査対象を正常品として判定される。 FIG. 4 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 3 and the frequency. The diagnosis unit 61 diagnoses the presence or absence of abnormality in the inspection object based on the analysis result of the data analysis unit 51, and outputs the diagnosis result. In FIG. 4, the width of the distribution is set to 0.1. Note that the width of the distribution may be another value. The average of the correlation coefficients for inspection objects #1 to #3, which are normal products, belongs to the frequency 402 of 0.8 to 0.9. On the other hand, the average correlation coefficient for inspection object #4, which is an abnormal product, belongs to the frequency 401 of 0.5 to 0.6. In the distribution of the average correlation coefficients for inspection objects #1 to #4, the average correlation coefficient for inspection object #4, which is an abnormal product, is an outlier. Therefore, it is possible to detect the average of test object #4 as an outlier by a clustering technique such as the mean shift method or hierarchical clustering. Inspection target #4 detected as an outlier is determined to be abnormal. On the other hand, since the inspection objects #1 to #3 are distributed in the same positions, they are determined as normal products. The above result is output as a diagnosis result of the presence or absence of abnormality. Note that when all the inspection objects are normal products, the frequencies are distributed at substantially the same position, so they are classified into the same class by clustering, and all the inspection objects are determined as normal products.
　図５は、実施の形態１に係る検査装置１１の動作を示すフローチャートである。データ収集部４１は、加振部２１によって加振された物体の振動を検出して振動の計測値を出力する振動検出部３１から、物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、振動の周波数ごとの計測値を計測データとして収集する（ステップＳ１）。データ解析部５１は、Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について計測データの相関係数を算出し、Ｎ個の検査対象の各々について相関係数の総和又は平均である評価値を算出する（ステップＳ２）。診断部６１は、Ｎ個の検査対象の各々の評価値を互いに比較し、比較の結果に基づいてＮ個の検査対象の各々に異常が有るか無いかを診断する（ステップＳ３）。 FIG. 5 is a flow chart showing the operation of the inspection device 11 according to the first embodiment. The data collection unit 41 detects N (N is an integer of 3 or more) inspections of objects from the vibration detection unit 31 that detects the vibration of the object vibrated by the vibrating unit 21 and outputs the vibration measurement value. Measured values for each vibration frequency are collected as measurement data for each object (step S1). The data analysis unit 51 calculates the correlation coefficient of the measurement data for each combination of two inspection objects out of the N inspection objects, and calculates the sum or average of the correlation coefficients for each of the N inspection objects. A certain evaluation value is calculated (step S2). The diagnosis unit 61 compares the evaluation values of the N inspection objects with each other, and diagnoses whether or not each of the N inspection objects has an abnormality based on the comparison result (step S3).
《１－３》効果
　実施の形態１に係る検査装置又は検査方法では、各検査対象の計測データに関する相関係数を算出するため、各振動ピークの周波数を算出する必要がない。したがって、各振動ピークの周波数の間隔が狭い場合又は計測データに雑音が含まれる場合においても高い診断精度を維持することができる。 <<1-3>> Effect In the inspection apparatus or inspection method according to the first embodiment, since the correlation coefficient regarding the measurement data of each inspection object is calculated, it is not necessary to calculate the frequency of each vibration peak. Therefore, high diagnostic accuracy can be maintained even when the intervals between the frequencies of vibration peaks are narrow or when noise is included in the measurement data.
　また、実施の形態１に係る検査装置又は検査方法では、多数が正常である検査対象の場合、正常な検査対象に関する相関係数の平均は、大きくなる。一方で、異常な検査対象に関する相関係数の平均は、小さくなる。そのため、予め正常と確認された対象物が無い場合であっても、各検査対象に関する相関係数の平均から外れ値の有無を判定することで、異常の有無の診断を行うことができる。 In addition, in the inspection apparatus or inspection method according to Embodiment 1, when a large number of inspection objects are normal, the average of the correlation coefficients for normal inspection objects becomes large. On the other hand, the average correlation coefficient for abnormal test objects is small. Therefore, even if there is no object that has been confirmed to be normal in advance, it is possible to diagnose the presence or absence of an abnormality by determining the presence or absence of an outlier from the average of the correlation coefficients for each inspection object.
　また、実施の形態１に係る検査装置又は検査方法では、振動検出部３１が検出する検査対象の振動の周波数を１ｋＨｚ以上の予め定められた周波数帯域とすることが望ましい。この場合には、以下の第１から第３の効果が得られる。 Also, in the inspection apparatus or inspection method according to Embodiment 1, it is desirable that the frequency of the vibration of the inspection target detected by the vibration detection unit 31 is set to a predetermined frequency band of 1 kHz or more. In this case, the following first to third effects are obtained.
　第１の効果は、数ｋＨｚ以上の振動を検出することによって、数百μｍから数ｍｍオーダのひび割れの検出が可能となることである。このことは、例えば、以下の非特許文献１に記載されている。 The first effect is that detecting vibrations of several kHz or higher makes it possible to detect cracks on the order of several hundred μm to several mm. This is described, for example, in Non-Patent Document 1 below.
　第２の効果は、予めひび割れによる応答変化が大きい周波数帯域が明らかになっている場合、数ｋＨｚ以上の周波数帯域の振動を検出することで診断精度を向上させることができることである。第３の効果は、ひび割れの検出にあまり適していない１ｋＨｚ未満の周波数を検出しないことで振動雑音比を向上させることができることである。 The second effect is that if the frequency band in which the response change due to cracking is large is known in advance, the diagnostic accuracy can be improved by detecting vibration in the frequency band of several kHz or higher. A third effect is that the vibration-to-noise ratio can be improved by not detecting frequencies below 1 kHz, which are not well suited for detecting cracks.
　加振器２１ａと振動検出器３１ａに圧電素子を用いることで、数ｋＨｚ以上での高周波数での加振が実現し、数ｍｍオーダ以下のひび割れの検出が可能となる。また、信号発生器等を用いることで加振力が安定するため、一般に加振力の安定性が低い打音検査などと比較して、計測精度の向上が可能となる。 By using a piezoelectric element for the vibration exciter 21a and the vibration detector 31a, vibration at a high frequency of several kHz or more is realized, and cracks of the order of several millimeters or less can be detected. In addition, since the excitation force is stabilized by using a signal generator or the like, it is possible to improve the measurement accuracy as compared with hammering inspection or the like in which the stability of the excitation force is generally low.
　また、実施の形態１に係る検査装置又は検査方法は、発電機の回転子ウェッジを点検する技術などの機械の狭小なスペース内に設置されている部品を点検する場合において、超音波探傷によって部品の異常の有無を診断する、非特許文献２に記載の技術と比較して、以下の第４から第６の効果を有する。 In addition, the inspection apparatus or inspection method according to Embodiment 1 can be used to inspect parts installed in a narrow space of a machine, such as a technique for inspecting a rotor wedge of a generator. It has the following fourth to sixth effects compared with the technique described in Non-Patent Document 2, which diagnoses the presence or absence of an abnormality.
　まず、第４の効果について説明する。超音波探傷では、検査対象である部品の各位置を全て計測する必要があり、検査時間が長くなるという問題がある。一方で、実施の形態１に係る検査装置又は検査方法を用いれば、部品単位の点検が可能であるため、点検時間を短縮することができる。 First, the fourth effect will be explained. In the ultrasonic flaw detection, it is necessary to measure all the positions of the parts to be inspected, and there is a problem that the inspection time is long. On the other hand, by using the inspection apparatus or inspection method according to Embodiment 1, it is possible to inspect each part, so inspection time can be shortened.
　次に、第５の効果について説明する。超音波探傷に用いる超音波センサの厚さは、一般に数十ｍｍ以上であるため、超音波センサの厚さと同等程度又はそれ未満の隙間しかないスペースでは、超音波センサによって検査することができないという問題がある。一方、実施の形態１に係る検査装置又は検査方法は、圧電素子を用いることができ、その厚さは、数ｍｍ以下であるため、より多くの検査対象に適用することができる。 Next, the fifth effect will be explained. Since the thickness of the ultrasonic sensor used for ultrasonic flaw detection is generally several tens of millimeters or more, it is said that the ultrasonic sensor cannot be used for inspection in a space with a gap equal to or less than the thickness of the ultrasonic sensor. There's a problem. On the other hand, the inspection apparatus or inspection method according to Embodiment 1 can use a piezoelectric element, and the thickness of the piezoelectric element is several millimeters or less, so it can be applied to a larger number of inspection objects.
　次に、第６の効果について説明する。超音波探傷において、検査対象が粉塵等で汚れている場合、センサと検査対象との間で超音波が大きく減衰し、検査精度が悪化する恐れがある。一方、実施の形態１に係る検査装置又は検査方法は、検査対象を圧電素子によって加振させる測定原理において、粉塵の有無は、検査精度に影響を与えない。したがって、検査対象が粉塵等で汚れている場合であっても高い検査精度を維持することができる。 Next, the sixth effect will be explained. In ultrasonic flaw detection, when an object to be inspected is contaminated with dust or the like, ultrasonic waves are greatly attenuated between the sensor and the object to be inspected, and there is a risk that inspection accuracy will deteriorate. On the other hand, in the inspection apparatus or inspection method according to Embodiment 1, the presence or absence of dust does not affect the inspection accuracy in the measurement principle in which the inspection object is vibrated by the piezoelectric element. Therefore, even if the object to be inspected is contaminated with dust or the like, high inspection accuracy can be maintained.
《１－４》変形例
　図６は、実施の形態１に係る検査システム１の加振部２１と振動検出部３１とを移動体７１に搭載した例を示す図である。検査システム１は、加振部２１、振動検出部３１を移動ロボットなどの移動体７１に搭載し、遠隔で計測することができるものでであってもよい。以上の構成によって、人では、アクセスすることのできない場所における検査を実現することができる。また、移動体による自動検査を行うことで、省人化を実現することができる。 <<1-4>> Modified Example FIG. 6 is a diagram showing an example in which the vibrating section 21 and the vibration detecting section 31 of the inspection system 1 according to the first embodiment are mounted on the moving body 71 . The inspection system 1 may be one in which the vibrating section 21 and the vibration detecting section 31 are mounted on a mobile body 71 such as a mobile robot, and measurements can be performed remotely. With the above configuration, it is possible to perform inspections in places that are inaccessible to humans. In addition, labor saving can be realized by performing automatic inspection using a moving body.
　発電機の回転子ウェッジを点検する技術などにおいて、移動体を挿入可能な隙間は、１００ｍｍ程度以下である。したがって、加振部２１と振動検出部３１の厚さを１００ｍｍ以下とすることで回転子ウェッジを点検可能な構成を実現できる。 　In the technology for inspecting the rotor wedge of a generator, the gap through which a moving object can be inserted is about 100 mm or less. Therefore, by setting the thickness of the vibrating portion 21 and the vibration detecting portion 31 to 100 mm or less, it is possible to realize a configuration that allows inspection of the rotor wedge.
　なお、データ解析部５１は、各検査対象の計測データとの相関係数は、予め定められた複数の周波数範囲のそれぞれにおいて算出した相関係数の平均としてもよい。他の振動モードと比較して卓越して振動強度の大きい振動モードが存在する場合、相関係数は、この振動モードの影響を大きく受け、その他の振動モードに異常の有無による応答変化が生じても影響を受けにくいという問題がある。そこで、複数の周波数帯域毎に相関係数を算出することで、振動モードの振動強度が大きい周波数帯域と、小さい帯域のそれぞれにおいて規格化された相関係数の算出ができる。このため、各振動モードの振動強度によらず影響を均等にした相関係数を算出することができる。 Note that the data analysis unit 51 may use the average of the correlation coefficients calculated in each of a plurality of predetermined frequency ranges as the correlation coefficient with the measurement data of each inspection target. If there is a vibration mode with a significantly higher vibration intensity than other vibration modes, the correlation coefficient is greatly affected by this vibration mode, and response changes occur depending on the presence or absence of abnormalities in other vibration modes. There is also the problem that it is less likely to be affected by Therefore, by calculating the correlation coefficient for each of a plurality of frequency bands, it is possible to calculate normalized correlation coefficients in each of the frequency band in which the vibration intensity of the vibration mode is large and the band in which the vibration intensity is small. Therefore, it is possible to calculate a correlation coefficient that equalizes the influence regardless of the vibration intensity of each vibration mode.
　なお、振動検出部３１が検出する情報は、振動強度でなく、加振部２１が検査対象を加振する信号の位相と振動検出部３１が検出する振動の位相の差など、位相に関する情報でもよい。また、振動検出部３１が検出する情報は、振動強度と加振部２１が検査対象を加振する信号の位相又は振動検出部３１が検出する振動の位相の差とを組合せた情報であってもよい。 Note that the information detected by the vibration detection unit 31 is not the vibration intensity, but may be phase information, such as the difference between the phase of the signal that vibrates the test object by the vibration excitation unit 21 and the phase of the vibration detected by the vibration detection unit 31. good. Further, the information detected by the vibration detection unit 31 is information obtained by combining the vibration intensity and the phase of the signal for vibrating the inspection object by the vibrating unit 21 or the phase difference of the vibration detected by the vibration detection unit 31. good too.
また、データ解析部５１は、Ｎ個の検査対象のうちの２個の検査対象の計測データの相関係数を算出する処理を、Ｎ個の検査対象のうちの２個の検査対象のすべてではなく、検査対象の各々に実行する形としてもよい。 In addition, the data analysis unit 51 performs the process of calculating the correlation coefficient of the measurement data of two inspection objects out of the N inspection objects for all two inspection objects out of the N inspection objects. Instead, it may be executed for each object to be inspected.
　なお、データ解析部５１は、相関係数ではなく、その他の類似度を算出するものであってもよい。例えば、データ解析部５１は、計測データ２０１～２０４の各周波数の振動強度を各次元の長さとして算出したユークリッド距離を使用してもよい。類似度は、相関係数又はユークリッド距離である。 Note that the data analysis unit 51 may calculate other degrees of similarity instead of the correlation coefficient. For example, the data analysis unit 51 may use the Euclidean distance calculated by using the vibration intensity of each frequency of the measurement data 201 to 204 as the length of each dimension. Similarity is a correlation coefficient or Euclidean distance.
　また、データ解析部５１は、類似度の平均でなく、総和、中央値、トリム平均などのその他の統計量を算出するものであってもよい。 In addition, the data analysis unit 51 may calculate other statistics such as the sum, the median, and the trimmed average instead of the similarity average.
　つまり、データ解析部５１は、Ｎ個の検査対象のうちの２個の検査対象の計測データの類似度を算出する処理を、Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、Ｎ個の検査対象の各々について類似度の統計量である評価値を算出する。 That is, the data analysis unit 51 performs the process of calculating the similarity of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects. is executed, and an evaluation value, which is a similarity statistic, is calculated for each of the N inspection targets.
　なお、診断部６１は、クラスタリングによって異常の有無を診断するのでなく、閾値判定によって異常の有無の診断を行ってもよい。 It should be noted that the diagnosis unit 61 may diagnose whether there is an abnormality by threshold determination instead of clustering.
　また、加振器２１ａは、ハンマーなどの検査対象に打撃を与えるものとして、また振動検出器３１ａをマイクロフォンなどの振動を検出し各周波数の振動強度を計測する構成であってもよい。 In addition, the vibration exciter 21a may be configured such that it strikes an object to be inspected such as a hammer, and the vibration detector 31a may be configured to detect the vibration of a microphone or the like and measure the vibration intensity of each frequency.
　また、加振器２１ａとしては、動電式加振器などを用いてもよい。 Also, an electrodynamic vibrator or the like may be used as the vibrator 21a.
　また、振動検出器３１ａは、加速度センサなどを用いてもよい。また、加振器２１ａは、接触して検査対象を加振するものではなく、レーザなどによって非接触に加振するものであってもよい。また、振動検出器３１ａについても接触して検査対象の振動を検出するものではなく、レーザなどによって非接触に振動を検出するものでよい。 Also, an acceleration sensor or the like may be used as the vibration detector 31a. Further, the vibrator 21a may vibrate the object to be inspected in a non-contact manner using a laser or the like, instead of vibrating the object to be inspected by contact. Further, the vibration detector 31a does not need to be in contact to detect the vibration of the object to be inspected.
《２》実施の形態２
《２－１》構成
　図７は、実施の形態２に係る検査装置１２及び検査システム２の構成を示す概略図である。図７に示されるように、検査システム２は、加振部２２と、振動検出部３１と、検査装置１２とを有している。検査装置１２は、データ収集部４２と、データ解析部５２と、診断部６２とを有している。検査装置１２は、実施の形態２に係る検査方法を実施することができる装置である。検査装置１２は、検査対象の異常の有無を診断する。 <<2>> Embodiment 2
<<2-1>> Configuration FIG. 7 is a schematic diagram showing configurations of an inspection apparatus 12 and an inspection system 2 according to the second embodiment. As shown in FIG. 7 , the inspection system 2 has a vibrator 22 , a vibration detector 31 and an inspection device 12 . The inspection device 12 has a data collection section 42 , a data analysis section 52 and a diagnosis section 62 . The inspection device 12 is a device capable of implementing the inspection method according to the second embodiment. The inspection device 12 diagnoses the presence or absence of an abnormality in the inspection object.
　加振部２２は、複数の加振器を有している。実施の形態２では、加振部２２が２個の加振器２２ａ、２２ｂを有している場合を説明する。２個の加振器２２ａ、２２ｂは、検査対象１００の異なる位置を加振する。 The vibrating section 22 has a plurality of vibrators. Embodiment 2 describes a case where the vibrating section 22 has two vibrators 22a and 22b. The two vibrators 22 a and 22 b vibrate different positions of the inspection target 100 .
　実施の形態２における振動検出部３１は、実施の形態１に記載のものと同じである。 The vibration detection section 31 in the second embodiment is the same as that described in the first embodiment.
　データ収集部４２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、振動検出部３１が検出した振動強度に関する計測データを収集する。 The data collection unit 42 collects measurement data related to the vibration intensity detected by the vibration detection unit 31 when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. .
　データ解析部５２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて計測された、３個以上の検査対象の計測データに関して、他の検査対象の計測データとの相関係数を算出し、その相関係数の平均を算出する。 The data analysis unit 52 analyzes the measurement data of the three or more inspection objects measured when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. , and the average of the correlation coefficients is calculated.
　診断部６２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、データ解析部５２が算出した各検査対象における相関係数の平均を比較分析して、検査対象ごとの異常の有無を診断し、その結果を出力する。 The diagnosis unit 62 calculates the average of the correlation coefficients of the inspection objects calculated by the data analysis unit 52 when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. are compared and analyzed, the presence or absence of abnormality is diagnosed for each inspection object, and the result is output.
《２－２》動作
　検査対象は、３個以上であればよいが、実施の形態２では、４個の検査対象＃１～＃４を検査する例について説明する。 <<2-2>> Operation Although the number of inspection objects may be three or more, an example in which four inspection objects #1 to #4 are inspected will be described in the second embodiment.
　図８（Ａ）及び（Ｂ）は、実施の形態２に係る検査システム２の加振部２２の加振器２２ａ、２２ｂによって加振された検査対象の振動モード３０１、３０２と振動検出器３１ａとを示す図である。加振部２２の加振器２２ａ、２２ｂは、１ｋＨｚ以上において予め定められた周波数帯域で加振を行う。一方の加振器が検査対象を加振しているときに、他方の加振器は検査対象を加振しない。 FIGS. 8A and 8B show vibration modes 301 and 302 of an object to be inspected excited by the vibrators 22a and 22b of the vibrating unit 22 of the inspection system 2 according to the second embodiment, and the vibration detector 31a. It is a figure which shows . The vibrators 22a and 22b of the vibrating section 22 vibrate in a predetermined frequency band of 1 kHz or higher. While one vibrator vibrates the test object, the other vibrator does not vibrate the test object.
　図９は、実施の形態２に係る検査システム２の加振器２２ａ、２２ｂと図８（Ａ）及び（Ｂ）に示される振動モード３０１、３２０との関係を示す図である。検査対象１００の振動モードの一部として、振動モード３０１、３０２の２種類がある場合を説明する。振動モード３０１は、設置箇所が振動モード３０１の節ではない加振器２２ａによって励起することができ、一方で、設置箇所が振動モード３０１の節である加振器２２ｂによっては、励起することができない。振動モード３０２は、設置箇所が振動モード３０２の節である加振器２２ａによっては、励起することができず、一方で、設置箇所が振動モード３０２の節でない加振器２２ｂによって励起することができる。図９は、加振器２２ａ、２２ｂのそれぞれが、励起することができる振動モード（丸印マーク「○」で示す）と、励起することができない振動モード（バツ印マーク「Ｘ」で示す）を表形式で示す。振動モード３０１は、加振器２２ａにより励起することができ、振動モード３０２は、加振器２２ｂにより励起することができ、両加振器を使用することで振動モード３０１、３０２の両方を励起することができる。振動検出部３１は、加振器２２ａが検査対象を加振した場合と加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、各検査対象を加振したときの振動強度を検出する。 FIG. 9 is a diagram showing the relationship between the vibration exciters 22a and 22b of the inspection system 2 according to Embodiment 2 and the vibration modes 301 and 320 shown in FIGS. 8(A) and 8(B). A case where there are two types of vibration modes 301 and 302 as part of the vibration modes of the inspection object 100 will be described. Vibration mode 301 can be excited by vibrator 22a whose installation location is not a node of vibration mode 301, while it can be excited by vibration exciter 22b whose installation location is a node of vibration mode 301. Can not. The vibration mode 302 cannot be excited by the vibration exciter 22a whose installation location is a node of the vibration mode 302, but can be excited by the vibration exciter 22b whose installation location is not a node of the vibration mode 302. can. FIG. 9 shows vibration modes that can be excited by each of the vibration exciters 22a and 22b (indicated by circle marks “◯”) and vibration modes that cannot be excited (indicated by cross marks “X”). are shown in tabular form. Vibrational mode 301 can be excited by shaker 22a and vibrational mode 302 can be excited by shaker 22b, using both shakers to excite both vibration modes 301, 302. can do. The vibration detection unit 31 detects the vibration intensity when each test object is vibrated when the vibrator 22a vibrates the test object and when the vibrator 22b vibrates the test object.
　図１０（Ａ）から（Ｄ）は、実施の形態２に係る検査装置１２のデータ収集部４２によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。データ収集部４２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、振動検出部３１が検出した振動強度に関する計測データを収集する。加振器２２ａが検査対象を加振した場合、データ収集部４２は、図２（Ａ）から（Ｄ）に示される計測データ２０１～２０４を取得する。また、加振器２２ｂが検査対象を加振した場合、データ収集部４２は、図１０（Ａ）から（Ｄ）に示される計測データ２０５～２０８を取得する。正常品である検査対象＃１～＃３の計測データ２０５～２０７は、概ね同じ傾向を示している。一方で、異常品である検査対象＃４の計測データ２０８は、検査対象＃１～＃３の計測データ２０５～２０８と比較して振動ピークの周波数又は振動ピークの数が異なっている。また、加振器２２ａ、２２ｂのそれぞれが検査対象を加振した場合、両者では、加振可能な振動モードが異なるため、計測データは、異なる。ひび割れは、その状態によって振動モードに与える影響が異なるため、ある振動モードには、影響を与えない一方で、他方の振動モードには、大きく影響を与える場合がある。そのため、加振器２２ａ、２２ｂの両方で加振した計測データを収集することで、正常品と異常品とで大きく応答が異なる計測データを取得しやすくなる。 10A to 10D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit 42 of the inspection apparatus 12 according to the second embodiment as frequency/vibration intensity characteristics. is. The data collection unit 42 collects measurement data related to the vibration intensity detected by the vibration detection unit 31 when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. . When the vibration exciter 22a vibrates the object to be inspected, the data collection unit 42 acquires measurement data 201 to 204 shown in FIGS. 2(A) to 2(D). Further, when the vibrator 22b vibrates the object to be inspected, the data collection unit 42 acquires the measurement data 205 to 208 shown in FIGS. 10(A) to 10(D). Measurement data 205 to 207 of inspection objects #1 to #3, which are normal products, show substantially the same tendency. On the other hand, the measurement data 208 of the inspection object #4, which is an abnormal product, differs from the measurement data 205 to 208 of the inspection objects #1 to #3 in the frequency of vibration peaks or the number of vibration peaks. Further, when the vibrators 22a and 22b vibrate the test object, the vibration modes that can be vibrated are different between the vibrators 22a and 22b, so that the measurement data are different. Since cracks have different effects on vibration modes depending on their state, there are cases where they do not affect one vibration mode, but greatly affect the other vibration mode. Therefore, by collecting measurement data obtained by applying vibrations by both the vibrators 22a and 22b, it becomes easier to acquire measurement data in which the responses of the normal product and the abnormal product are significantly different.
　図１１は、実施の形態２に係る検査装置１２のデータ解析部５２によって行われる処理を示す図である。データ解析部５２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、実施の形態１の場合と同様に解析を行う。実施の形態１で示した通り、加振器２２ａが検査対象を加振した場合、検査対象＃１～＃３に関する相関係数の平均は、０．８３～０．８６の範囲である。また、検査対象＃４に関する相関係数の平均は、０．５９である。一方で、加振器２２ｂが検査対象を加振した場合の解析結果は、図１１のようになる。計測データ＃１～＃３に関する相関係数の平均は、０．８０～０．８１の範囲にある一方で、検査対象＃４に関する相関係数の平均は、０．４８となり、検査対象＃１～＃３に関する相関係数の平均と大きく異なる。 FIG. 11 is a diagram showing processing performed by the data analysis unit 52 of the inspection device 12 according to the second embodiment. The data analysis unit 52 performs analysis in the same manner as in the first embodiment when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. As shown in Embodiment 1, when the vibration exciter 22a vibrates the test object, the average correlation coefficient for the test objects #1 to #3 is in the range of 0.83 to 0.86. Also, the average correlation coefficient for test object #4 is 0.59. On the other hand, the analysis result when the vibration exciter 22b vibrates the object to be inspected is as shown in FIG. The average correlation coefficient for measurement data #1 to #3 is in the range of 0.80 to 0.81, while the average correlation coefficient for test object #4 is 0.48. significantly different from the average correlation coefficient for ~#3.
　図１２は、図１１に示される相関係数の平均と度数との関係を示す度数分布図である。診断部６２は、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、データ解析部５２の解析結果を元に異常の有無を診断し、その診断結果を出力する。加振器２２ａが検査対象を加振した場合の異常の有無の判定結果については、実施の形態１で説明した通りである。加振器２２ｂが検査対象を加振した場合の検査対象＃１～＃４に関する相関係数の平均の分布を図１２に示す。正常品である検査対象＃１～＃３に関する相関係数の平均は、０．８～０．９の度数４０４に属している。一方で、異常品である検査対象＃４に関する相関係数の平均は、０．４～０．５の度数４０３に属している。実施の形態１の場合と同様にクラスタリングに関する手法によって、検査対象＃４は、異常と判定される。なお、加振器２２ｂが検査対象を加振した場合の方が、正常品である検査対象＃１～＃３と、異常品である検査対象＃４との相関係数の平均の差異が大きいことが確認できる。つまり、加振器２２ｂが検査対象を加振した場合の方が、異常の有無を診断する精度は高い。以上のように、複数個の加振器を用いることで、診断精度を向上させることができる。 FIG. 12 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 11 and the frequency. The diagnosis unit 62 diagnoses the presence or absence of an abnormality based on the analysis result of the data analysis unit 52 when the vibration exciter 22a vibrates the inspection object and when the vibration exciter 22b vibrates the inspection object. , and outputs its diagnostic results. The determination result of the presence/absence of an abnormality when the vibration exciter 22a vibrates the object to be inspected is as described in the first embodiment. FIG. 12 shows the average distribution of the correlation coefficients for the inspection objects #1 to #4 when the vibration exciter 22b vibrates the inspection object. The average of the correlation coefficients for inspection objects #1 to #3, which are normal products, belongs to the frequency 404 of 0.8 to 0.9. On the other hand, the average correlation coefficient for inspection object #4, which is an abnormal product, belongs to the frequency 403 of 0.4 to 0.5. Inspection object #4 is determined to be abnormal by a clustering method as in the first embodiment. It should be noted that when the vibration exciter 22b vibrates the inspection object, the average difference in the correlation coefficients between the inspection objects #1 to #3, which are normal products, and the inspection object #4, which is an abnormal product, is larger. can be confirmed. In other words, the accuracy of diagnosing the presence or absence of an abnormality is higher when the vibration exciter 22b vibrates the object to be inspected. As described above, the diagnosis accuracy can be improved by using a plurality of vibrators.
《２－３》効果
　実施の形態２に係る検査装置１２及び検査方法を用いれば、実施の形態１に記載の効果に加え、複数個の加振器を用いることで、異常に関してより感度の高い計測データを収集し、より正常品と異常品とで相関係数の異なる結果を得やすくなるため、診断精度を向上させることができる。 <<2-3>> Effect By using the inspection apparatus 12 and the inspection method according to the second embodiment, in addition to the effects described in the first embodiment, by using a plurality of vibration exciters, it is possible to achieve higher sensitivity with respect to abnormalities. By collecting measurement data, it becomes easier to obtain results with different correlation coefficients between normal products and abnormal products, so that diagnostic accuracy can be improved.
　なお、今回は、加振器２２ａ、２２ｂの両方が検査対象を加振した場合において、異常の有無を正しく診断することができたが、計測データに大きな雑音が生じている場合、診断精度は、悪化する恐れがある。以上の影響によって、加振器２２ａが検査対象を加振した場合において、異常の有無を正しく判断できず異常品を検出できない恐れがある。このため、実施の形態２に係る検査装置１２及び検査方法は、雑音が少ない場合の使用に適している。また、加振器２２ｂが検査対象を加振した場合において、正常品と異常品とで加振器２２ａが検査対象を加振した場合よりも相関係数の差異が大きくなるため、正しく異常の有無を判定することができる。 In this case, when both the vibrators 22a and 22b vibrated the object to be inspected, the presence or absence of abnormality could be correctly diagnosed. , may worsen. Due to the effects described above, when the vibration exciter 22a vibrates the object to be inspected, there is a possibility that the presence or absence of an abnormality cannot be correctly determined and an abnormal product cannot be detected. Therefore, the inspection apparatus 12 and inspection method according to the second embodiment are suitable for use when there is little noise. In addition, when the vibration exciter 22b vibrates the inspection target, the difference in the correlation coefficient between the normal product and the abnormal product becomes larger than when the vibration exciter 22a vibrates the inspection target. Presence or absence can be determined.
　なお、実施の形態２では、２個の加振器を使用したが、３個以上の加振器を使用してもよい。また、１個の加振器を使用して異なる位置で複数回計測することで、複数個の加振器を使用したときと同様のデータを取得してもよい。 Although two vibrators are used in the second embodiment, three or more vibrators may be used. Also, by using one vibration exciter and measuring multiple times at different positions, the same data as when using a plurality of vibration exciters may be obtained.
《３》実施の形態３
《３－１》構成
　図１３は、実施の形態３に係る検査装置１３及び検査システム３の構成を示す概略図である。図１３に示されるように、検査システム３は、加振部２２と、振動検出部３１と、検査装置１３とを有している。検査装置１３は、データ収集部４３と、データ解析部５３と、診断部６３とを有している。検査装置１３は、実施の形態３に係る検査方法を実施することができる装置である。検査装置１３は、検査対象の異常の有無を診断する。 <<3>> Embodiment 3
<<3-1>> Configuration FIG. 13 is a schematic diagram showing the configuration of the inspection apparatus 13 and the inspection system 3 according to the third embodiment. As shown in FIG. 13 , the inspection system 3 has a vibrator 22 , a vibration detector 31 and an inspection device 13 . The inspection device 13 has a data collection section 43 , a data analysis section 53 and a diagnosis section 63 . The inspection device 13 is a device capable of implementing the inspection method according to the third embodiment. The inspection device 13 diagnoses the presence or absence of abnormality in the inspection object.
　加振部２２、振動検出部３１は、実施の形態２のものと同じである。データ解析部５３は、同一の検査対象に対して計測した複数の計測データにおいて、各周波数における振動強度の最大値を選定して作成した周波数・振動強度特性を用いて相関係数の平均を算出する。診断部６３は、実施の形態１における診断部６１と同じである。 The vibrating section 22 and the vibration detecting section 31 are the same as those in the second embodiment. The data analysis unit 53 calculates the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency in a plurality of measurement data measured for the same inspection object. do. Diagnosis section 63 is the same as diagnosis section 61 in the first embodiment.
《３－３》動作
　検査対象は、３個以上であればよいが、実施の形態３では、４個の検査対象＃１～＃４を検査する例について説明する。加振部２２の加振器２２ａ、２２ｂは、実施の形態２の場合の場合と同様に１ｋＨｚ以上において予め定められた周波数帯域で加振を行う。振動検出部３１は、実施の形態２の場合と同様に、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、各検査対象を加振したときの振動強度を検出する。 <<3-3>> Operation Although the number of inspection objects may be three or more, an example in which four inspection objects #1 to #4 are inspected will be described in the third embodiment. The vibrators 22a and 22b of the vibrating section 22 vibrate in a predetermined frequency band of 1 kHz or more as in the case of the second embodiment. As in the case of the second embodiment, the vibration detection unit 31 vibrates each inspection target when the vibration exciter 22a vibrates the inspection target and when the vibration exciter 22b vibrates the inspection target. Detects the vibration intensity when shaken.
　データ収集部４３は、実施の形態２の場合と同様に、加振器２２ａが検査対象を加振した場合及び加振器２２ｂが検査対象を加振した場合のそれぞれにおいて、振動検出部３１が検出した振動強度に関する計測データを収集する。 As in the case of the second embodiment, the data collection unit 43 detects that the vibration detection unit 31 is Collect measurement data on the detected vibration intensity.
　データ解析部５３は、同一の検査対象に対して計測した複数の計測データにおいて、各周波数における振動強度の最大値を選定して作成した周波数・振動強度特性を用いて相関係数の平均を算出する。図１４（Ａ）から（Ｄ）は、実施の形態３に係る検査装置のデータ収集部によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。検査対象＃１～＃４の計測データ２０１～２０４は、加振器２２ａが検査対象を加振した場合にデータ収集部４３が収集した計測データである。検査対象＃１～＃４の計測データ２０５～２０８は、加振器２２ｂが検査対象を加振した場合にデータ収集部４３が収集した計測データである。両者の各周波数の振動強度は同様の振動ピークを検出する一方で、異なる振動ピークを検出している場合もある。 The data analysis unit 53 calculates the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency in a plurality of measurement data measured for the same inspection object. do. 14A to 14D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit of the inspection apparatus according to Embodiment 3 as frequency/vibration intensity characteristics. . The measurement data 201 to 204 of the inspection objects #1 to #4 are the measurement data collected by the data collection unit 43 when the vibration exciter 22a vibrates the inspection object. The measurement data 205 to 208 of the inspection objects #1 to #4 are the measurement data collected by the data collection unit 43 when the vibration exciter 22b vibrates the inspection object. While the vibration intensity of each frequency of both detects a similar vibration peak, it may detect a different vibration peak.
　図１５（Ａ）から（Ｄ）は、実施の形態３に係る検査装置１３のデータ収集部４３によって収集された検査対象＃１～＃４の計測データ２０９～２１２の振動強度の最大値を周波数・振動強度特性として示す図である。これによって、両方の計測データが検出していた振動ピークを検出し、異常部分によって応答が変化する振動ピークを捉えることができる。次に、作成した周波数・振動強度特性を、実施の形態１の場合と同様の方法によって、各周波数・振動強度特性に関する相関係数の平均を算出する。 15A to 15D show the maximum values of the vibration intensity of the measurement data 209 to 212 of the inspection objects #1 to #4 collected by the data collection unit 43 of the inspection apparatus 13 according to the third embodiment. - It is a figure shown as a vibration strength characteristic. As a result, it is possible to detect the vibration peak detected by both measurement data, and capture the vibration peak whose response changes depending on the abnormal portion. Next, the average of the correlation coefficients for each frequency/vibration intensity characteristic is calculated using the same method as in the first embodiment.
　診断部６３は、実施の形態１の場合と同様に、データ解析部５３の解析結果を元に異常の有無を診断し、その診断結果を出力する。 As in the case of the first embodiment, the diagnosis unit 63 diagnoses whether there is an abnormality based on the analysis result of the data analysis unit 53, and outputs the diagnosis result.
《３－３》効果
　実施の形態３に係る検査装置１３及び検査方法を用いれば、実施の形態１に記載の効果に加え、各周波数において複数の測定結果の最大値を選定することで、加振位置、計測位置によって発生する振動モードの検出漏れを抑制し、診断精度が向上する。 <<3-3>> Effects By using the inspection apparatus 13 and the inspection method according to the third embodiment, in addition to the effects described in the first embodiment, by selecting the maximum value of a plurality of measurement results at each frequency, It suppresses omissions in the detection of vibration modes that occur depending on the vibration position and measurement position, improving diagnostic accuracy.
　なお、実施の形態３では、２個の加振器を使用したが、３個以上を使用してもよい。 Although two vibrators are used in Embodiment 3, three or more vibrators may be used.
《４》実施の形態４
《４－１》構成
　図１６は、実施の形態４に係る検査装置１４及び検査システム４の構成を示す概略図である。図１６に示されるように、検査システム４は、加振部２１と、振動検出部３２と、検査装置１４とを有している。検査装置１４は、データ収集部４４と、データ解析部５４と、診断部６４とを有している。検査装置１４は、実施の形態４に係る検査方法を実施することができる装置である。検査装置１４は、検査対象の異常の有無を診断する。検査装置１４は、加振部２１と、振動検出部３２と、データ収集部４４と、データ解析部５４と、診断部６４と、から構成され、検査対象の異常の有無を診断する。 <<4>> Embodiment 4
<<4-1>> Configuration FIG. 16 is a schematic diagram showing configurations of an inspection apparatus 14 and an inspection system 4 according to the fourth embodiment. As shown in FIG. 16 , the inspection system 4 has a vibrator 21 , a vibration detector 32 and an inspection device 14 . The inspection device 14 has a data collection section 44 , a data analysis section 54 and a diagnosis section 64 . The inspection device 14 is a device capable of implementing the inspection method according to the fourth embodiment. The inspection device 14 diagnoses the presence or absence of abnormality in the inspection object. The inspection device 14 is composed of a vibrating section 21, a vibration detection section 32, a data collection section 44, a data analysis section 54, and a diagnosis section 64, and diagnoses the presence or absence of abnormality in the inspection target.
　加振部２１は、実施の形態１で記載のものと同じである。振動検出部３２は、複数の振動検出器を有している。実施の形態４では、振動検出部３２は、振動検出器３２ａ、３２ｂを有している。 The vibrating section 21 is the same as that described in the first embodiment. The vibration detector 32 has a plurality of vibration detectors. In Embodiment 4, the vibration detector 32 has vibration detectors 32a and 32b.
　データ収集部４４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、振動強度に関する計測データを収集する。 The data collection unit 44 collects measurement data related to vibration intensity when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration.
　データ解析部５４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、３個以上の検査対象の計測データのそれぞれに関して、他の検査対象の計測データとの相関係数を算出し、その相関係数の平均を算出する。 When the vibration detector 32a detects vibrations and when the vibration detector 32b detects vibrations, the data analysis unit 54 determines the measurement data of the three or more test targets for each of the measurement data of the other test targets. Calculate the correlation coefficient with the data, and calculate the average of the correlation coefficients.
　診断部６４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、データ解析部５４が算出した各検査対象における相関係数の平均を比較分析して、検査対象ごとの異常の有無を診断し、その結果を出力する。 The diagnosis unit 64 compares and analyzes the average correlation coefficients calculated by the data analysis unit 54 for each test object when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration. Then, the presence or absence of abnormality is diagnosed for each inspection object, and the result is output.
《４－２》動作
　検査対象は、３個以上であればよいが、実施の形態４では、実施の形態１の場合と同様に、４個の検査対象＃１～＃４を検査する例について説明する。加振部２１の加振器２１ａは、実施の形態１の場合と同様に１ｋＨｚ以上において予め定められた周波数帯域で加振を行う。 <<4-2>> Operation The number of inspection objects may be three or more. explain. The vibrator 21a of the vibrating section 21 vibrates in a predetermined frequency band of 1 kHz or higher, as in the case of the first embodiment.
　図１７（Ａ）及び（Ｂ）は、実施の形態４に係る検査システム４の加振器２１ａによって加振された検査対象の振動モードと振動検出器３２ａ、３２ｂとを示す図である。振動検出部３２の振動検出器３２ａ、３２ｂのそれぞれは、各検査対象を加振したときの振動強度を検出する。実施の形態２の場合と同様に、検査対象１００の振動モードの一部として、振動モード３０１、振動モード３０２の２種類がある場合を説明する。振動モード３０１は、設置箇所が振動モード３０１の節ではない振動検出器３２ａによって検出することができる一方で、設置箇所が振動モード３０１の節である振動検出器３２ｂによっては検出することができない。振動モード３０２は、設置箇所が振動モード３０２の節である振動検出器３２ａによっては、検出することができない一方で、設置箇所が振動モード３０２の節でない振動検出器３２ｂによっては、検出することができる。 FIGS. 17A and 17B are diagrams showing the vibration modes of the inspection object excited by the vibration exciter 21a of the inspection system 4 according to Embodiment 4 and the vibration detectors 32a and 32b. Each of the vibration detectors 32a and 32b of the vibration detection unit 32 detects vibration intensity when each inspection object is vibrated. As in the case of the second embodiment, a case where there are two types of vibration modes 301 and 302 as part of the vibration modes of the inspection object 100 will be described. The vibration mode 301 can be detected by the vibration detector 32a whose installation location is not the node of the vibration mode 301, but cannot be detected by the vibration detector 32b whose installation location is the node of the vibration mode 301. The vibration mode 302 cannot be detected by the vibration detector 32a whose installation location is the node of the vibration mode 302, but can be detected by the vibration detector 32b whose installation location is not the node of the vibration mode 302. can.
　図１８は、実施の形態４に係る検査システムの振動検出器と図１７（Ａ）及び（Ｂ）に示される振動モードとの関係を示す図である。振動モード３０１、３０２は、それぞれ振動検出器３２ａ、３２ｂのみにより検出可能であり、両振動検出器３２ａ、３２ｂを使用することで振動モード３０１、３０２の両方の振動を検出することができる。 FIG. 18 is a diagram showing the relationship between the vibration detector of the inspection system according to Embodiment 4 and the vibration modes shown in FIGS. 17(A) and 17(B). Vibration modes 301 and 302 can be detected only by vibration detectors 32a and 32b, respectively, and both vibration modes 301 and 302 can be detected by using both vibration detectors 32a and 32b.
　図１９（Ａ）から（Ｄ）は、実施の形態４に係る検査装置のデータ収集部４４によって収集された検査対象＃１～＃４の計測データの例を周波数・振動強度特性として示す図である。データ収集部４４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、振動強度に関する計測データを収集する。振動検出器３２ａが振動を検出した場合、図２（Ａ）から（Ｄ）に示される計測データ２０１～２０４を得られる。また、振動検出器３２ｂが振動を検出した場合、図１９（Ａ）から（Ｄ）に示される計測データ２１３～２１６が得られる。 FIGS. 19A to 19D are diagrams showing examples of measurement data of inspection objects #1 to #4 collected by the data collection unit 44 of the inspection apparatus according to the fourth embodiment as frequency/vibration intensity characteristics. be. The data collection unit 44 collects measurement data regarding vibration intensity when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration. When the vibration detector 32a detects vibration, measurement data 201 to 204 shown in FIGS. 2(A) to 2(D) are obtained. When the vibration detector 32b detects vibration, measurement data 213 to 216 shown in FIGS. 19A to 19D are obtained.
　正常品である検査対象＃１～＃３の計測データ２１３～２１５は、概ね同じ傾向を示している。一方で、異常品である検査対象＃４の計測データ２１６は、検査対象＃１～＃３の計測データ２１３～２１５と比較して振動ピークの周波数又は振動ピークの数が異なっている。また、振動検出器３２ａ、３２ｂのそれぞれが振動を検出した場合、両者では、検出可能な振動モードが異なるため、計測データは、異なる。ひび割れは、その状態によって振動モードに与える影響が異なるため、ある振動モードには影響を与えない一方で、他方の振動モードには、大きく影響を与える場合がある。そのため、振動検出器３２ａ、３２ｂの両方で検出した計測データを収集することで、正常品と異常品とで大きく応答が異なる計測データを取得しやすくなる。 The measurement data 213 to 215 of inspection objects #1 to #3, which are normal products, show roughly the same tendency. On the other hand, the measurement data 216 of the inspection object #4, which is an abnormal product, differs from the measurement data 213 to 215 of the inspection objects #1 to #3 in the frequency of vibration peaks or the number of vibration peaks. Further, when each of the vibration detectors 32a and 32b detects vibration, the two detectable vibration modes are different, so the measurement data are different. Cracks have different effects on vibration modes depending on the state of the cracks. Therefore, there are cases where cracks do not affect a certain vibration mode, but greatly affect the other vibration mode. Therefore, by collecting the measurement data detected by both the vibration detectors 32a and 32b, it becomes easier to acquire the measurement data whose responses are significantly different between the normal product and the abnormal product.
　図２０は、実施の形態４に係る検査装置１４のデータ解析部５４によって行われる処理を示す図である。データ解析部５４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、実施の形態１の場合と同様に解析を行う。実施の形態１で示した通り、振動検出器３２ａが振動を検出した場合、検査対象＃１～＃３に関する相関係数の平均は、０．８３～０．８６の範囲である。また、検査対象＃４に関する相関係数の平均は、０．５９である。一方で、振動検出器３２ｂが振動を検出した場合の解析結果を図２０に示す。計測データ＃１～＃３に関する相関係数の平均は、０．８１である一方で、検査対象＃４に関する相関係数の平均は、０．４８となり、検査対象＃１～＃３に関する相関係数の平均と大きく異なる。 FIG. 20 is a diagram showing processing performed by the data analysis unit 54 of the inspection device 14 according to the fourth embodiment. The data analysis unit 54 performs analysis in the same manner as in the first embodiment when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration. As shown in the first embodiment, when the vibration detector 32a detects vibration, the average correlation coefficient for the inspection objects #1 to #3 is in the range of 0.83 to 0.86. Also, the average correlation coefficient for test object #4 is 0.59. On the other hand, FIG. 20 shows the analysis result when the vibration detector 32b detects vibration. The average correlation coefficient for the measurement data #1 to #3 is 0.81, while the average correlation coefficient for the test object #4 is 0.48. Very different from the number average.
　図２１は、図２０に示される相関係数の平均と度数との関係を示す度数分布図である。診断部６４は、振動検出器３２ａが振動を検出した場合及び振動検出器３２ｂが振動を検出した場合のそれぞれにおいて、データ解析部５４の解析結果を元に異常の有無を診断し、その診断結果を出力する。振動検出器３２ａが振動を検出した場合の異常の有無の判定結果については、実施の形態１で説明した通りである。図２１に示されるように、正常品である検査対象＃１～＃３に関する相関係数の平均は、０．８～０．９の度数４０６に属している。一方で、異常品である検査対象＃４に関する相関係数の平均は、０．４～０．５の度数４０５に属している。実施の形態１の場合と同様にクラスタリングに関する手法によって、検査対象＃４は、異常と判定される。なお、振動検出器３２ｂが振動を検出した場合の方が、正常品である検査対象＃１～＃３と、異常品である検査対象＃４との相関係数の平均の差異が大きいことが確認できる。つまり、振動検出器３２ｂが振動を検出した場合の方が、異常の有無を診断する精度は高い。以上のように、複数個の振動検出器を用いることで、診断精度を向上させることができる。 FIG. 21 is a frequency distribution diagram showing the relationship between the average of the correlation coefficients shown in FIG. 20 and the frequency. The diagnosis unit 64 diagnoses the presence or absence of an abnormality based on the analysis results of the data analysis unit 54 when the vibration detector 32a detects vibration and when the vibration detector 32b detects vibration, and the diagnosis result is to output The determination result of the presence or absence of abnormality when the vibration detector 32a detects vibration is as described in the first embodiment. As shown in FIG. 21, the average correlation coefficients for inspection objects #1 to #3, which are normal products, belong to the frequency 406 of 0.8 to 0.9. On the other hand, the average correlation coefficient for inspection object #4, which is an abnormal product, belongs to the frequency 405 of 0.4 to 0.5. Inspection object #4 is determined to be abnormal by a clustering method as in the first embodiment. It should be noted that when the vibration detector 32b detects vibration, the average difference in the correlation coefficients between the inspection objects #1 to #3, which are normal products, and the inspection object #4, which is an abnormal product, is larger. I can confirm. That is, when the vibration detector 32b detects vibration, the accuracy of diagnosing the presence or absence of abnormality is higher. As described above, by using a plurality of vibration detectors, the accuracy of diagnosis can be improved.
《４－３》効果
　実施の形態４に係る検査装置１４及び検査方法によれば、実施の形態１に記載の効果に加え、複数個の振動検出器を用いることで、異常に関してより感度の高い計測データを収集し、より正常品と異常品とで相関係数の異なる結果を得やすくなるため、診断精度を向上させることができる。なお、振動検出器３２ａ、３２ｂが振動を検出した両方の場合において、異常の有無を正しく診断することができる例を説明したが、計測データに大きな雑音が生じている場合には、診断精度は悪化する恐れがある。このため、実施の形態４に係る検査装置１４及び検査方法は、雑音が少ない場合の使用に適している。 <<4-3>> Effect According to the inspection apparatus 14 and the inspection method according to the fourth embodiment, in addition to the effects described in the first embodiment, by using a plurality of vibration detectors, the sensitivity to abnormalities is higher. By collecting measurement data, it becomes easier to obtain results with different correlation coefficients between normal products and abnormal products, so that diagnostic accuracy can be improved. An example has been described in which the presence or absence of an abnormality can be correctly diagnosed in both cases where the vibration detectors 32a and 32b detect vibration. It may get worse. Therefore, the inspection apparatus 14 and inspection method according to the fourth embodiment are suitable for use when there is little noise.
　以上の影響によって、振動検出器３２ａが振動を検出した場合において、異常の有無を正しく判断できず異常品を検出できない恐れがある。一方で、振動検出器３２ｂが振動を検出した場合において、正常品と異常品とで振動検出器３２ａが振動を検出した場合よりも相関係数の差異が大きくなるため、依然正しく異常の有無を判定することができる。 Due to the above effects, when the vibration detector 32a detects vibration, there is a risk that the presence or absence of an abnormality cannot be determined correctly and an abnormal product cannot be detected. On the other hand, when the vibration detector 32b detects vibration, the difference in the correlation coefficient between the normal product and the abnormal product is greater than when the vibration detector 32a detects vibration. can judge.
《４－４》変形例
　なお、実施の形態４では、２個の振動検出器を使用したが、３個以上の振動検出器を使用してもよい。また、実施の形態４では、１個の加振器を使用したが、実施の形態２で示したように２個以上の加振器を使用してもよい。この場合、２個以上の加振器と、２個以上の振動検出器との組み合わせの数に応じた計測データを得ることができる。また、各１個の加振器と振動検出器を使用して異なる位置で複数回計測することで、複数個の加振器と振動検出器を使用したときと同様のデータを取得してもよい。 <<4-4>> Modification Although two vibration detectors are used in the fourth embodiment, three or more vibration detectors may be used. Moreover, although one vibrator is used in the fourth embodiment, two or more vibrators may be used as shown in the second embodiment. In this case, measurement data corresponding to the number of combinations of two or more vibration exciters and two or more vibration detectors can be obtained. In addition, by using one vibration exciter and vibration detector and measuring multiple times at different positions, the same data as when using multiple vibration exciters and vibration detectors can be obtained. good.
　また、実施の形態４では、実施の形態３のように、データ解析部５４は、振動検出器３２ａ、３２ｂそれぞれが検出した場合の計測データにおいて、同一の検査対象に対して計測した複数の計測データの各周波数における振動強度の最大値を選定して作成した周波数・振動強度特性を用いて相関係数の平均を算出する方法によって異常の有無の診断を実施してもよい。この場合、診断部６４は、実施の形態１のように、データ解析部５４の解析結果を元に異常の有無を診断し、その診断結果を出力する構成となる。 Further, in the fourth embodiment, as in the third embodiment, the data analysis unit 54 uses the measurement data detected by the vibration detectors 32a and 32b to measure a plurality of measurements of the same inspection target. The presence or absence of abnormality may be diagnosed by a method of calculating the average correlation coefficient using the frequency/vibration intensity characteristics created by selecting the maximum value of the vibration intensity at each frequency of the data. In this case, the diagnosis unit 64 diagnoses whether there is an abnormality based on the analysis result of the data analysis unit 54 and outputs the diagnosis result, as in the first embodiment.
《５》実施の形態５
《５－１》構成
　図２２は、実施の形態５に係る検査装置１５及び検査システム５の構成を示す概略図である。図２２に示されるように、検査システム５は、加振部２１と、振動検出部３１と、検査装置１５とを有している。検査装置１５は、データ収集部４５と、データ解析部５５と、診断部６５とを有している。検査装置１５は、実施の形態５に係る検査方法を実施することができる装置である。検査装置１５は、検査対象の異常の有無を診断する。 <<5>> Embodiment 5
<<5-1>> Configuration FIG. 22 is a schematic diagram showing configurations of an inspection apparatus 15 and an inspection system 5 according to the fifth embodiment. As shown in FIG. 22 , the inspection system 5 has a vibrator 21 , a vibration detector 31 and an inspection device 15 . The inspection device 15 has a data collection section 45 , a data analysis section 55 and a diagnosis section 65 . The inspection device 15 is a device capable of implementing the inspection method according to the fifth embodiment. The inspection device 15 diagnoses the presence or absence of abnormality in the inspection object.
　検査システム５において、加振部２１の加振器２１ａと振動検出部３１の振動検出器３１ａは、検査対象１０１～１０６を搬送方向に搬送する搬送ライン上に設置されている。検査装置１５は、データ収集部４５と、データ解析部５５と、診断部６５とを有しており、検査対象の異常の有無を診断する。加振部２１、振動検出部３１、データ収集部４５は、実施の形態１に記載のものと同様である。データ解析部５５は、データ収集部４５によって収集された計測データのうちの、現時点に近い予め定められた個数の検査対象、つまり、直近に収集した予め定められた個数の検査対象の相関係数の評価値を算出する処理を実行する。検査対象は、３個以上であればよいが、データ解析部５５は、直近にデータ収集した４個の検査対象に対して、実施の形態１の場合と同様に、計測データを解析する。診断部６５は、直近に収集した４個の検査対象に対して、実施の形態１の場合と同様にデータ解析部５５の解析結果を元に異常の有無を診断し、その診断結果を出力する。 In the inspection system 5, the vibration exciter 21a of the vibration excitation unit 21 and the vibration detector 31a of the vibration detection unit 31 are installed on a transport line that transports the inspection objects 101 to 106 in the transport direction. The inspection device 15 has a data collection unit 45, a data analysis unit 55, and a diagnosis unit 65, and diagnoses the presence or absence of abnormality in the inspection object. The vibrating section 21, the vibration detecting section 31, and the data collecting section 45 are the same as those described in the first embodiment. The data analysis unit 55 calculates the correlation coefficient of a predetermined number of test objects close to the current time, that is, the predetermined number of test objects collected most recently, among the measurement data collected by the data collection unit 45. Execute the process of calculating the evaluation value of . Although the number of inspection targets may be three or more, the data analysis unit 55 analyzes the measurement data of the four inspection targets from which data has been collected most recently, in the same manner as in the first embodiment. Diagnosis unit 65 diagnoses the presence or absence of abnormalities in the four most recently collected test objects based on the analysis results of data analysis unit 55 in the same manner as in the first embodiment, and outputs the diagnosis results. .
《５－２》動作
　実施の形態５では、一例として搬送ライン上の検査対象１０１～１０６を検査する例について説明する。加振部２１は、搬送ライン上を流れる各検査対象に対して、実施の形態１の場合と同様に予め定められた１ｋＨｚ以上の周波数帯域で検査対象を加振する。 <<5-2>> Operation In the fifth embodiment, an example of inspecting inspection objects 101 to 106 on a transfer line will be described. The vibrating section 21 vibrates each test object flowing on the carrier line in a predetermined frequency band of 1 kHz or more, as in the case of the first embodiment.
　振動検出部３１は、搬送ライン上を流れる各検査対象に対して、実施の形態１の場合と同様に、各検査対象を加振したときの振動強度を検出する。 The vibration detection unit 31 detects the vibration intensity of each inspection object flowing on the transfer line when each inspection object is vibrated, as in the case of the first embodiment.
　データ収集部４５は、実施の形態１の場合と同様に、振動検出部３１が検出した振動強度に関する計測データを収集する。 The data collection unit 45 collects measurement data related to the vibration intensity detected by the vibration detection unit 31, as in the case of the first embodiment.
　データ解析部５５は、直近に収集した４個の検査対象に対して、実施の形態１の場合と同様に計測データを解析する。したがって、検査対象１０４に関する計測データを収集すると、直近に得られた検査対象１０１～１０４に関する計測データを解析する。 The data analysis unit 55 analyzes the measurement data of the four most recently collected inspection targets in the same manner as in the first embodiment. Therefore, when the measurement data regarding the inspection object 104 is collected, the most recently obtained measurement data regarding the inspection objects 101 to 104 are analyzed.
　診断部６５は、直近に収集した４個の検査対象に対して、実施の形態１の場合と同様に、データ解析部５５の解析結果を元に検査対象の異常の有無を診断し、その診断結果を出力する。 Diagnosis unit 65 diagnoses the presence or absence of abnormalities in the recently collected four inspection objects based on the analysis result of data analysis unit 55, as in the case of the first embodiment, and performs the diagnosis. Print the result.
　以上の処理を、各検査対象に関する計測データを収集する度に実施していくことで、搬送ライン上を流れる検査対象の異常の有無を診断する。 By performing the above process each time measurement data for each inspection object is collected, the presence or absence of abnormalities in the inspection objects flowing on the transport line can be diagnosed.
《５－３》効果
　実施の形態５に係る検査装置１５及び検査方法を用いれば、実施の形態１に記載の効果に加え、検査対象の計測データを収集する度に、異常の有無の判定が可能となるためインライン検査を実現することができるという効果がある。 <<5-3>> Effects By using the inspection apparatus 15 and the inspection method according to the fifth embodiment, in addition to the effects described in the first embodiment, the presence or absence of an abnormality can be determined each time the measurement data of the inspection target is collected. Since it becomes possible, there is an effect that in-line inspection can be realized.
　なお、実施の形態５では、直近に収集した４個の検査対象を用いて異常の有無の診断を実施したが、異常の有無を診断する検査対象の個数は、３個以上であれば何個であってもよい。 In the fifth embodiment, diagnosis of the presence or absence of an abnormality is performed using the four most recently collected inspection objects. may be
《６》ハードウェア構成
　図２３は、実施の形態１から５に係る検査装置１１～１５及び検査システム１～５のハードウェア構成の例を示す図である。図２３の例では、実施の形態１から５に係る検査装置１１～１５は、ＣＰＵなどのプロセッサ５０１と、記憶装置としてのメモリ５０２と、インタフェース５０３とを有している。加振部２１（又は２２）及び振動検出部３１（又は３２）は、インタフェース５０３に接続されている。プロセッサ５０１は、メモリ５０２に記憶されているソフトウェアとしてのプログラム（例えば、実施の形態１から５に係る検査プログラム）を実行する。検査装置１１～１５は、コンピュータであってもよい。 <<6>> Hardware Configuration FIG. 23 is a diagram showing an example of the hardware configuration of the inspection apparatuses 11 to 15 and the inspection systems 1 to 5 according to the first to fifth embodiments. In the example of FIG. 23 , the inspection apparatuses 11 to 15 according to Embodiments 1 to 5 have a processor 501 such as a CPU, a memory 502 as a storage device, and an interface 503 . The vibrating section 21 (or 22) and the vibration detecting section 31 (or 32) are connected to the interface 503. FIG. The processor 501 executes programs as software stored in the memory 502 (for example, inspection programs according to Embodiments 1 to 5). The inspection devices 11-15 may be computers.
　図２４は、実施の形態１から５に係る検査装置１１～１５及び検査システム１～５のハードウェア構成の他の例を示す図である。図２４の例では、実施の形態１から５に係る検査装置１１～１５は、処理回路５０４と、インタフェース５０３とを有している。加振部２１（又は２２）及び振動検出部３１（又は３２）は、インタフェース５０３に接続されている。処理回路５０４は、例えば、実施の形態１から５に係る検査装置１１～１５を構成する半導体集積回路、システムＬＳＩ、ＦＰＧＡ（ｆｉｅｌｄ－ｐｒｏｇｒａｍｍａｂｌｅ　ｇａｔｅ　ａｒｒａｙ）などである。また、１つ以上のプロセッサ、１つ以上のメモリ、及び処理回路が連携して検査装置１１～１５の機能を実現してもよい。 FIG. 24 is a diagram showing another example of the hardware configuration of the inspection apparatuses 11-15 and inspection systems 1-5 according to Embodiments 1-5. In the example of FIG. 24, the inspection apparatuses 11 to 15 according to Embodiments 1 to 5 have a processing circuit 504 and an interface 503. FIG. The vibrating section 21 (or 22) and the vibration detecting section 31 (or 32) are connected to the interface 503. FIG. The processing circuit 504 is, for example, a semiconductor integrated circuit, a system LSI, or a field-programmable gate array (FPGA) that constitutes the inspection apparatuses 11 to 15 according to the first to fifth embodiments. Also, one or more processors, one or more memories, and processing circuits may cooperate to implement the functions of the inspection devices 11-15.
　１～５　検査システム、　１１～１５　検査装置、　２１、２２　加振部、　２１ａ、２２ａ、２２ｂ　加振器、　３１、３２　振動検出部、　３１ａ、３２ａ、３２ｂ　振動検出器、　４１～４５　データ収集部、　５１～５５　データ解析部、　６１～６５　診断部、　７１　移動体、　１００～１０６　検査対象、　２０１～２１６　計測データ、　３０１、３０２　振動モードの振幅、　４０１～４０６　相関係数の平均の分布の度数。 1 to 5 inspection system, 11 to 15 inspection device, 21, 22 vibration part, 21a, 22a, 22b vibration exciter, 31, 32 vibration detection part, 31a, 32a, 32b vibration detector, 41 to 45 data collection part , 51 to 55 Data analysis part, 61 to 65 Diagnosis part, 71 Moving body, 100 to 106 Inspection object, 201 to 216 Measurement data, 301, 302 Amplitude of vibration mode, 401 to 406 Average distribution frequency of correlation coefficient .

Claims (17)

  1. 　加振部によって加振された物体の振動を検出して前記振動の計測値を出力する振動検出部から、前記物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、前記振動の周波数ごとの前記計測値を計測データとして収集するデータ収集部と、
    　前記Ｎ個の検査対象のうちの２個の検査対象の前記計測データの類似度を算出する処理を、前記Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、前記Ｎ個の検査対象の各々について前記類似度の統計量である評価値を算出するデータ解析部と、
    　前記Ｎ個の検査対象の各々の前記評価値を互いに比較し、前記比較の結果に基づいて前記Ｎ個の検査対象の各々に異常が有るか無いかの診断を行う診断部と、
    　を有することを特徴とする検査装置。     For each of N (N is an integer equal to or greater than 3) inspection objects as the objects, the above-mentioned a data collection unit that collects the measured values for each vibration frequency as measurement data;
    performing a process of calculating the similarity of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects, a data analysis unit that calculates an evaluation value, which is a statistic of the similarity, for each of N inspection objects;
    a diagnostic unit that compares the evaluation values of the N test objects with each other and diagnoses whether or not each of the N test objects has an abnormality based on the comparison result;
    An inspection device comprising:
  2. 　前記類似度は、相関係数又はユークリッド距離である
    　ことを特徴とする請求項１に記載の検査装置。     The inspection apparatus according to claim 1, wherein the degree of similarity is a correlation coefficient or a Euclidean distance.
  3. 　前記統計量は、総和又は平均又は中央値又はトリム平均である
    　ことを特徴とする請求項１又は２に記載の検査装置。     The inspection apparatus according to claim 1 or 2, wherein the statistic is sum, average, median, or trimmed average.
  4. 　前記データ収集部は、前記計測値として、前記周波数ごとの前記振動の強度又は位相の少なくとも一方を収集する
    　ことを特徴とする請求項１から３のいずれか１項に記載の検査装置。     The inspection apparatus according to any one of claims 1 to 3, wherein the data collection unit collects at least one of the intensity and the phase of the vibration for each frequency as the measured value.
  5. 　前記診断部は、前記Ｎ個の検査対象の各々の前記評価値の範囲に対する前記範囲に含まれる前記類似度の度数との関係を示す度数分布に基づいて前記診断を行う
    　ことを特徴とする請求項１から４のいずれか１項に記載の検査装置。     The diagnosis unit performs the diagnosis based on a frequency distribution showing the relationship between the range of the evaluation values of each of the N inspection objects and the frequency of the degree of similarity included in the range. Item 5. The inspection device according to any one of Items 1 to 4.
  6. 　前記データ収集部は、予め定められた周波数帯域の振動を検出する前記振動検出部から出力された前記計測値を前記計測データとして収集する
    　ことを特徴とする請求項１から５のいずれか１項に記載の検査装置。     6. The data collection unit collects, as the measurement data, the measured values output from the vibration detection unit that detects vibrations in a predetermined frequency band. The inspection device described in .
  7. 　前記データ収集部は、前記加振部に含まれる複数の加振器によって前記Ｎ個の検査対象の各々の異なる位置に付与された振動の周波数ごとの前記計測値を、前記複数の加振器の各々の計測データとして収集する
    　ことを特徴とする請求項１から６のいずれか１項に記載の検査装置。     The data collection unit collects the measured values for each frequency of vibration applied to different positions of the N inspection objects by a plurality of vibrators included in the vibrating unit, using the plurality of vibrators. 7. The inspection apparatus according to any one of claims 1 to 6, wherein the measurement data is collected for each of the.
  8. 　前記データ解析部は、前記複数の加振器の各々の前記計測データから前記振動の周波数ごとに前記振動の強度が大きい前記計測データを選択し、選択された前記周波数ごとの計測データによって作成された周波数・振動強度特性を用いて前記類似度を算出する前記処理を実行する
    　ことを特徴とする請求項７に記載の検査装置。     The data analysis unit selects, from the measurement data of each of the plurality of vibrators, the measurement data having a large vibration intensity for each frequency of the vibration, and creates the measurement data for each of the selected frequencies. 8. The inspection apparatus according to claim 7, wherein the process of calculating the degree of similarity is performed using the frequency/vibration intensity characteristics obtained from the frequency/vibration intensity characteristic.
  9. 　前記データ収集部は、前記振動検出部に含まれる複数の振動検出器によって前記Ｎ個の検査対象の各々の異なる位置で計測された前記計測値を前記計測データとして収集する
    　ことを特徴とする請求項１から７のいずれか１項に記載の検査装置。     The data collection unit collects, as the measurement data, the measured values measured at different positions of each of the N inspection objects by a plurality of vibration detectors included in the vibration detection unit. Item 8. The inspection device according to any one of Items 1 to 7.
  10. 　前記データ解析部は、前記複数の振動検出器の各々の前記計測データから前記振動の周波数ごとに前記振動の強度が大きい前記計測データを選択し、選択された前記周波数ごとの計測データによって作成された周波数・振動強度特性を用いて前記類似度を算出する前記処理を実行する
    　ことを特徴とする請求項９に記載の検査装置。     The data analysis unit selects, from the measurement data of each of the plurality of vibration detectors, the measurement data having a large vibration intensity for each frequency of the vibration, and creates the measurement data for each of the selected frequencies. 10. The inspection apparatus according to claim 9, wherein the process of calculating the degree of similarity is performed using the frequency/vibration intensity characteristics obtained from the frequency/vibration intensity characteristic.
  11. 　前記データ解析部は、前記データ収集部によって収集された前記計測データのうちの、現時点に近い予め定められた個数の検査対象の前記類似度の前記評価値を算出する処理を実行する
    　ことを特徴とする請求項１から１０のいずれか１項に記載の検査装置。     The data analysis unit performs a process of calculating the evaluation value of the degree of similarity of a predetermined number of inspection targets close to the current time, among the measurement data collected by the data collection unit. The inspection device according to any one of claims 1 to 10.
  12. 　請求項１から１１のいずれか１項に記載の検査装置と、
    　前記加振部と、
    　前記振動検出部と、
    　を有することを特徴とする検査システム。     an inspection apparatus according to any one of claims 1 to 11;
    the vibrating section;
    the vibration detection unit;
    An inspection system comprising:
  13. 　前記加振部と前記振動検出部とを保持する移動体をさらに有し、
    　前記移動体の移動によって、前記加振部によって加振される位置と前記振動検出部によって振動が検出される位置とを変更する
    　ことを特徴とする請求項１２に記載の検査システム。     further comprising a moving body that holds the vibration excitation unit and the vibration detection unit;
    13. The inspection system according to claim 12, wherein the movement of the moving body changes a position where the vibration is applied by the vibration applying unit and a position where the vibration is detected by the vibration detection unit.
  14. 　前記加振部の厚さは、１００ｍｍ以下であり、
    　前記振動検出部の厚さは、１００ｍｍ以下である
    　ことを特徴とする請求項１２又は１３に記載の検査システム。     The vibrating portion has a thickness of 100 mm or less,
    The inspection system according to claim 12 or 13, wherein the thickness of the vibration detection section is 100 mm or less.
  15. 　前記加振部は、圧電素子を含み、
    　前記振動検出部は、圧電素子を含む
    　ことを特徴とする請求項１２から１４のいずれか１項に記載の検査システム。     The vibrating unit includes a piezoelectric element,
    The inspection system according to any one of claims 12 to 14, wherein the vibration detection section includes a piezoelectric element.
  16. 　加振部によって加振された物体の振動を検出して前記振動の計測値を出力する振動検出部から、前記物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、前記振動の周波数ごとの前記計測値を計測データとして収集するステップと、
    　前記Ｎ個の検査対象のうちの２個の検査対象の前記計測データの類似度を算出する処理を、前記Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、前記Ｎ個の検査対象の各々について前記類似度の統計量である評価値を算出するステップと、
    　前記Ｎ個の検査対象の各々の前記評価値を互いに比較し、前記比較の結果に基づいて前記Ｎ個の検査対象の各々に異常が有るか無いかの診断を行うステップと、
    　を有することを特徴とする検査方法。     For each of N (N is an integer equal to or greater than 3) inspection objects as the objects, the above-mentioned a step of collecting the measured values for each vibration frequency as measurement data;
    performing a process of calculating the similarity of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects, a step of calculating an evaluation value, which is a statistic of the similarity, for each of N inspection objects;
    a step of comparing the evaluation values of each of the N inspection objects with each other and diagnosing whether or not each of the N inspection objects has an abnormality based on the result of the comparison;
    An inspection method characterized by having
  17. 　加振部によって加振された物体の振動を検出して前記振動の計測値を出力する振動検出部から、前記物体としてのＮ個（Ｎは３以上の整数）の検査対象の各々について、前記振動の周波数ごとの前記計測値を計測データとして収集するステップと、
    　前記Ｎ個の検査対象のうちの２個の検査対象の前記計測データの類似度を算出する処理を、前記Ｎ個の検査対象のうちの２個の検査対象の組合せの各々について実行し、前記Ｎ個の検査対象の各々について前記類似度の統計量である評価値を算出するステップと、
    　前記Ｎ個の検査対象の各々の前記評価値を互いに比較し、前記比較の結果に基づいて前記Ｎ個の検査対象の各々に異常が有るか無いかの診断を行うステップと、
    　をコンピュータに実行させることを特徴とする検査プログラム。     For each of N (N is an integer equal to or greater than 3) inspection objects as the objects, the above-mentioned a step of collecting the measured values for each vibration frequency as measurement data;
    performing a process of calculating the similarity of the measurement data of two inspection objects out of the N inspection objects for each combination of two inspection objects out of the N inspection objects, a step of calculating an evaluation value, which is a statistic of the similarity, for each of N inspection objects;
    a step of comparing the evaluation values of each of the N inspection objects with each other and diagnosing whether or not each of the N inspection objects has an abnormality based on the result of the comparison;
    An inspection program characterized by causing a computer to execute
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