WO2023286247A1 - Dispositif d'inspection, système d'inspection, procédé d'inspection, et programme d'inspection - Google Patents

Dispositif d'inspection, système d'inspection, procédé d'inspection, et programme d'inspection 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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English (en)
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 JP2023534548A priority Critical patent/JP7486672B2/ja
Priority to PCT/JP2021/026640 priority patent/WO2023286247A1/fr
Publication of WO2023286247A1 publication Critical patent/WO2023286247A1/fr

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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

L'invention concerne un dispositif d'inspection (11) qui comprend : une unité de collecte de données (41) qui collecte, en tant que données de mesure, des valeurs de mesure pour chaque fréquence de vibration pour chacun parmi N (où N est un nombre entier supérieur ou égal à 3) objets en cours d'inspection provenant d'une unité de détection de vibration (31) qui délivre en sortie des valeurs de mesure de vibration par la détection d'une vibration dans les objets, qui sont mis en vibration par une unité de vibration (21) ; une unité d'analyse de données (51) qui, pour chaque combinaison de deux objets en cours d'inspection parmi les N objets en cours d'inspection, effectue un traitement pour calculer la similarité des données de mesure pour les deux objets en cours d'inspection parmi les N objets en cours d'inspection, et calcule des valeurs d'évaluation qui sont des statistiques de similarité pour chacun des N objets en cours d'inspection ; et une unité de diagnostic (61) qui compare les valeurs d'évaluation pour les N objets en cours d'inspection les unes avec les autres et utilise le résultat de la comparaison pour produire un diagnostic pour chacun des N objets en cours d'inspection pour savoir si celui-ci est anormal.
PCT/JP2021/026640 2021-07-15 2021-07-15 Dispositif d'inspection, système d'inspection, procédé d'inspection, et programme d'inspection WO2023286247A1 (fr)

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PCT/JP2021/026640 WO2023286247A1 (fr) 2021-07-15 2021-07-15 Dispositif d'inspection, système d'inspection, procédé d'inspection, et programme d'inspection

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008232763A (ja) * 2007-03-19 2008-10-02 Toyota Motor Corp 欠陥検出装置および欠陥検出方法
JP2015064376A (ja) * 2014-12-04 2015-04-09 三菱電機株式会社 クラック検査装置
WO2015071925A1 (fr) * 2013-11-12 2015-05-21 日本電気株式会社 Dispositif d'analyse, procédé d'analyse, et programme d'analyse
JP2021032822A (ja) * 2019-08-28 2021-03-01 カヤバ システム マシナリー株式会社 検査装置の異常箇所評価システムおよび検査装置の異常箇所評価方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008232763A (ja) * 2007-03-19 2008-10-02 Toyota Motor Corp 欠陥検出装置および欠陥検出方法
WO2015071925A1 (fr) * 2013-11-12 2015-05-21 日本電気株式会社 Dispositif d'analyse, procédé d'analyse, et programme d'analyse
JP2015064376A (ja) * 2014-12-04 2015-04-09 三菱電機株式会社 クラック検査装置
JP2021032822A (ja) * 2019-08-28 2021-03-01 カヤバ システム マシナリー株式会社 検査装置の異常箇所評価システムおよび検査装置の異常箇所評価方法

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