JP2014238334A - Structure degradation diagnosis system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve reliability of diagnosis by providing a failure diagnosis function for a sensor head.SOLUTION: In order to determine in degradation diagnosis that a plurality of sensor heads are not in an abnormal condition, a sensor controller (10) individually calculates history data for sensor diagnosis acquired from acceleration information, and determines the sensor heads as abnormal, if a comparison value calculated as a ratio of the history data for sensor diagnosis individually calculated for two of the sensor heads is not within an acceptable range.

Description

  The present invention is capable of monitoring the deterioration of the mounting state of a structure such as a suspended structure in a tunnel or an overhang structure over a long period of time, and has a structure deterioration with a sensor fault diagnosis function. It relates to a diagnostic system.

  As a method for detecting the deterioration state of the structure, it has been the mainstream that the inspection is carried out by visual inspection or some kind of instrument by a regular inspection by an inspector. In addition, a conventional technique is disclosed in which a quantitative inspection is easily and quickly performed on a crack that occurs over time in a structure such as a tunnel that is a degradation diagnosis target (see, for example, Patent Document 1).

  In Patent Document 1, a fluorescent dye that emits light by excitation light such as ultraviolet light or blue-based visible light is mixed in advance with a structure that is an object of deterioration diagnosis. Then, the structure is irradiated with a light source that emits ultraviolet light, blue-based visible light, or the like, and the occurrence of cracks is quantitatively determined by visual analysis or analysis processing of a captured image by a CCD camera or the like.

JP 2013-83493 A

However, the prior art has the following problems.
Although Patent Document 1 enables quantitative deterioration diagnosis, it is based on periodic inspection by an inspector to the last. Furthermore, it is necessary to mix a fluorescent dye in advance with respect to the structure to be deteriorated.

  On the other hand, in recent years, there has been a demand for a deterioration diagnosis system that can perform deterioration diagnosis of a structure with a shorter cycle than a periodic inspection and without an inspector. In addition, in tunnels, in addition to the deterioration of the walls and ceiling of the tunnel itself, it is necessary to quantitatively diagnose the deterioration of the mounting state of a suspended structure in the tunnel or a structure such as an overhang structure. There is sex. Furthermore, it is desired that not only a new structure but also an existing structure can be easily handled.

  The present invention was made to solve the above problems, and can quantitatively diagnose the deterioration of the mounting state of the structure without the need for periodic inspection by an inspector, An object of the present invention is to obtain a structure deterioration diagnosis system having a sensor failure diagnosis function.

  The structure deterioration diagnosis system according to the present invention is based on acceleration information of a structure attached to a fixed surface and output from each of a plurality of sensor heads installed in the structure that is a deterioration diagnosis target. A structure deterioration diagnosis system having a sensor controller for diagnosing whether or not the mounting state is normal, the structure deterioration diagnosis system executed by the sensor controller, based on acceleration information acquired from each of a plurality of sensor heads Based on the feature quantity extraction unit that individually extracts at least one of the tilt information of the structure and the natural frequency information for each of the plurality of sensor heads as the feature quantity, and the feature quantity extracted by the feature quantity extraction unit The reference data obtained in the normal state and the diagnostic data obtained at the time of deterioration diagnosis A determination unit that determines that an abnormality has occurred in the mounting state of the structure, and the determination unit determines that a plurality of sensor heads are not in an abnormal state at the time of deterioration diagnosis In addition, the sensor diagnosis history data obtained from the acceleration information is individually calculated, and among the plurality of sensor heads, the first sensor diagnosis history data calculated for the first sensor head and the second sensor head are calculated. A comparison value that is a ratio with the calculated second sensor diagnosis history data is calculated, and at least the first sensor head or the second sensor when the calculated comparison value is not within a predetermined allowable range. One of the heads is determined to be in an abnormal state.

  According to the present invention, by detecting the degree of change in the probability density distribution calculated from the feature amount based on the acceleration information, it is possible to perform quantitative deterioration diagnosis and the detection results of a plurality of sensor heads at the time of deterioration diagnosis By comparing the historical data comparison value based on the deviation from the allowable range, the sensor head itself can be diagnosed, and the deterioration of the mounting state of the structure can be quantified without the need for periodic inspection by an inspector. Thus, it is possible to obtain a structure deterioration diagnosis system having a sensor failure diagnosis function.

It is a block diagram of the structure deterioration diagnostic system in Embodiment 1 of this invention. It is a flowchart which shows the flow of the degradation diagnosis process by the 1st degradation diagnostic part in Embodiment 1 of this invention. It is the figure which showed the specific example regarding collection of the inclination information for 3 axes in Embodiment 1 of this invention. It is the figure which showed the specific example of probability density distribution regarding the inclination information for 3 axes in Embodiment 1 of this invention. It is the figure which showed the specific example of probability density distribution regarding a certain one-axis inclination information, probability density ratio, and an evaluation value in Embodiment 1 of this invention. It is a flowchart which shows the flow of the degradation diagnostic process by the 2nd degradation diagnostic part in Embodiment 1 of this invention. It is the figure which showed the specific example regarding collection of the frequency information for 3 axes | shafts in Embodiment 1 of this invention. It is the figure which showed the calculation result of the power spectrum for 3 axes in Embodiment 1 of this invention. It is the figure in Embodiment 1 of this invention which showed the specific example of the probability density distribution at the time of learning calculated about a certain axis | shaft, the probability density distribution at the time of deterioration diagnosis, and the probability density ratio which is ratio of both.

Hereinafter, a preferred embodiment of a structure deterioration diagnosis system of the present invention will be described with reference to the drawings.
The present invention obtains a probability density distribution of a feature amount related to the inclination or natural frequency of a structure based on acceleration information obtained from a sensor attached to the structure to be diagnosed for deterioration, If there is a significant difference from the probability density distribution at the time of deterioration diagnosis, it is judged that some deterioration has occurred in the mounting state, and a sensor failure diagnosis function is provided.

Embodiment 1 FIG.
FIG. 1 is a configuration diagram of a structure deterioration diagnosis system according to Embodiment 1 of the present invention. The structure deterioration diagnosis system according to the first embodiment includes a sensor controller 10 and N (N is an integer of 2 or more) sensor heads 20 (1) to 20 (N).

  In the structure deterioration diagnosis system according to the first embodiment, it is possible to perform the deterioration diagnosis if at least one sensor head 20 is provided. In addition, the functions of the N sensor heads when a plurality of sensor heads are used are all common. Therefore, in the following description, when it is not necessary to distinguish each sensor head, the sensor head 20 is simply described without using the subscripts (1) to (N).

  Each of the N sensor heads 20 includes a sensor unit 21 and an acceleration information output unit 22, and is installed at different positions of the structure that is the object of the deterioration diagnosis. Here, specific examples of the structure include a suspended structure in a tunnel or an overhang structure, and the structure deterioration diagnosis system according to the present invention performs deterioration diagnosis of the mounting state of the structure over a long period of time. It will be.

  Examples of such a structure include jet fans, information boards, signs, and the like installed in the tunnel, and heavy objects that are suspended or overhang from the concrete of the casing forming the tunnel. Then, preferably two sensor heads 20 are installed on the structure, and each of them is installed at a portion which becomes both ends with respect to the structure, so that one of the displacements of the structure is largely captured.

  Therefore, the N sensor heads 20 are installed at positions away from each other with respect to the direction of the installation surface in order to capture the displacement of the structure greatly. Furthermore, the sensor head 20 may be replaced with two or more in order to facilitate replacement, and the monitoring state can be continued by replacing each pair.

  The sensor unit 21 is, for example, a three-axis acceleration sensor that uses a thin-film crystal resonator, has excellent responsiveness, and can measure acceleration with a measurement range of about DC to several tens of Hz. Thus, by using a triaxial sensor as the acceleration sensor, leveling is not required, and sensor output can be performed regardless of the direction of inclination or vibration. Therefore, if the direction of inclination can be specified such as leveling, two or one axis may be used.

  The acceleration information output unit 22 converts an analog signal related to the triaxial acceleration of the structure at the installation location of the sensor head into a digital signal at a predetermined sampling rate (for example, a sampling rate of 50 Hz), and the sensor is used as acceleration information. Transmit to the controller 10. In addition, the acceleration information output part 22 can transmit the acceleration information which is a digital signal to the sensor controller 10 via communication I / F, such as CAN and RS232C, as an example.

  The sensor controller 10 is installed at a position where the wiring reaches the N sensor heads 20 in the vicinity of the structure, and the mounting state of the structure is normal based on the acceleration information acquired from each of the N sensor heads 20. Determine whether or not. Specifically, the sensor controller 10 extracts the inclination information as the first feature amount based on the acceleration information, and performs the deterioration diagnosis of the attachment state of the structure, and the acceleration information. Based on this, it has the second deterioration diagnosis unit 12 that extracts the natural frequency information as the second feature quantity and performs the deterioration diagnosis of the attachment state of the structure.

  In addition, the sensor controller 10 includes a large-capacity communication I / F such as Ethernet (registered trademark), for example, so that the diagnosis result or data used for the diagnosis can be transmitted to a control device (not shown). Can do.

  In the case of a tunnel facility provided with a jet fan or the like as a structure, information in the tunnel is collected in a control device installed in the vicinity of the tunnel, which is provided in the vicinity of the tunnel, and is not shown in detail. The information is transferred to the remote monitoring and control device where there is, and the information is shared by both parties. In accordance with the deterioration diagnosis process described later, the result of determining that the deterioration has occurred is notified to each of the control devices with a display, an alarm sound, or the like. Such a determination result may be sent separately to a center apparatus of an equipment contractor or an inspection contractor of the deterioration diagnosis system, and can promptly respond when an abnormality occurs.

Further, the level for determining deterioration may be subdivided as follows, and the correspondence may be changed according to the level. For example, the deterioration judgment level is
Level 1: “Necessary to conduct detailed inspections at regular inspections (confirmation level)”
Level 2: “Need to check the site and consider future improvement plans (plan level)”
Level 3: “There is a need for immediate improvement, and there is a need to urgently go to the site (improvement level)”
Level 4: Emergency situation including collapse (emergency level) ”
Levels 1 and 2 can be simply displayed on the panel of each control device, and at levels 3 and 4, detailed alarm display and alarm sounding may be performed. .

  Furthermore, data used for continuous determination at the center device of the supplier may be collected and analyzed separately from multiple aspects, for example, to predict the time until reaching level 3. Tunnels are often monitored by a remote monitoring and control device at all times. However, in some cases, and in cases where daily management facilities are not sufficient, the primary emergency in the event of an emergency through the contractor's center device. It is also possible to make a staff member of the dealer take a response to confirm the situation and report the situation as a response.

  The first deterioration diagnosis unit 11 and the second deterioration diagnosis unit 12 both compare the probability density distribution at the normal time of the structure to be monitored with the probability density distribution at the time of deterioration diagnosis, and are significant. This is common in that the deterioration of the structure is detected due to the difference. However, a specific signal processing method is used in the first deterioration diagnosis unit 11 that performs the deterioration diagnosis using the inclination information as the feature amount and the second deterioration diagnosis unit 12 that performs the deterioration diagnosis using the natural frequency information as the feature amount. These are different and will be described in detail below.

(1) Flow of deterioration diagnosis processing based on inclination information by first deterioration diagnosis unit 11 FIG. 2 is a flowchart showing a flow of deterioration diagnosis processing by the first deterioration diagnosis unit 11 according to Embodiment 1 of the present invention. It is. A specific deterioration diagnosis process by the first deterioration diagnosis unit 11 based on the inclination information will be described based on the flowchart of FIG.

  First, in step S <b> 201, the first deterioration diagnosis unit 11 extracts inclination information based on the acceleration information acquired from the acceleration information output unit 22 in the sensor head 20. Specifically, the first deterioration diagnosis unit 11 acquires X, Y, and Z-axis acceleration information at a sampling rate of 50 Hz, for example. And the 1st degradation diagnostic part 11 performs the low-pass filter process by a digital filter for every axis | shaft using this acceleration information. The cut-off frequency of the low-pass filter is, for example, about 0.1 Hz.

  Here, the low-frequency component of the triaxial acceleration including the DC component is equivalent to the tilt information. Then, the low-pass filtered slope information does not include a rapidly changing high frequency component. For this reason, information is not lost even if the sampling rate of the inclination data for detecting the deterioration is slowed. Specifically, if there is a sampling rate of about twice the cutoff frequency, the sampling theorem can be satisfied. For a cutoff frequency of 0.1 Hz, a sampling frequency of 0.2 Hz, that is, 5 seconds. Sampling at intervals is sufficient.

  FIG. 3 is a diagram showing a specific example relating to collection of inclination information for three axes in the first embodiment of the present invention. As shown in FIG. 3, the first deterioration diagnosis unit 11 collects inclination information for three axes at each sampling interval ΔT. The “tilt information” in the present invention means a tilt in a range from 180 degrees to −180 degrees when a certain reference direction is defined as 0 degrees, and is shown on the vertical axis in FIG. “Inclination information” is obtained by normalizing the inclination from 180 degrees to −180 degrees with respect to the reference direction from 1 to −1.

  Next, the first deterioration diagnosis unit 11 corrects the mounting direction of the sensor head 20 in step S202. Specifically, the first deterioration diagnosis unit 11 performs a matrix operation on the calibration data (affine transformation matrix elements) in the mounting direction of the sensor head 20 and the sampling data obtained in the previous step S201. The error in the mounting direction is corrected so that the coordinate axes of the respective sensor heads 20 coincide. Here, when the sensor head 20 is replaced and a new sensor head is used, the calibration data must be replaced with data for the new sensor head.

  The purpose of this step S202 is to align the coordinate axes regardless of the mounting direction of the sensor head 20, and since the sensor head 20 can detect tilt and vibration regardless of the direction with three axes, the sensor output is used as it is. Is not necessary.

  Next, in step S203, the first deterioration diagnosis unit 11 performs logarithmic processing (decibelization) on the inclination information after correcting the error in the mounting direction of the sensor head 20. For measurement of minute inclination information, for example, if an attempt is made to obtain a probability density distribution, which will be described later, based on a resolution of 1/1 million [G] units, the storage area required for this is enormous. Become. Therefore, the first deterioration diagnosis unit 11 of the first embodiment performs efficient quantization by logarithmizing the acceleration (decibelization) and obtaining the probability density distribution by classification based on the decibel data. Yes.

  Next, in step S <b> 204, the first deterioration diagnosis unit 11 performs a probability density distribution generation process in order to perform deterioration diagnosis based on inclination information. For example, the first deterioration diagnosis unit 11 creates a cumulative frequency distribution in units of 0.5 decibels using the slope information decibeled in the previous step S203, and performs a local smoothing process on the cumulative frequency distribution. Generate a probability density distribution. As the smoothing process, for example, a Gaussian function can be applied.

  The probability density distribution as the initial learning data at the time of installation is generated using, for example, data for about one month. On the other hand, the number of data of the probability density distribution created as current data at the time of deterioration diagnosis is generated based on data of the past 3 hours to 24 hours, for example.

  Further, it is not always necessary to continuously use the data acquired at the time of installation as the learning data at the normal time serving as the reference data. Since deterioration diagnosis is performed over a long period of time, it is possible to continuously update learning data in the process of performing deterioration diagnosis. And the probability density distribution adopted as continuous learning data at the time of deterioration monitoring is generated based on data of the past several hours to several weeks, for example.

  FIG. 4 is a diagram showing a specific example of the probability density distribution regarding the inclination information for three axes in the first embodiment of the present invention. The one-dimensional probability density distribution of each axis is expressed as a distribution that spreads from the midpoint to both sides as the positive slope and the negative slope increase with respect to the midpoint without slope.

  Next, in step S205, the first deterioration diagnosis unit 11 converts the probability density distribution of each axis obtained at the time of deterioration diagnosis (corresponding to the data at the time of diagnosis) into the probability density distribution of each axis obtained at the time of learning (reference data) The ratio between the two is calculated, and probability density ratio distribution data is generated for each axis. As reference data, learning data updated by continuous learning data can be adopted in addition to the data at the time of initial setting.

  FIG. 5 is a diagram showing a specific example of probability density distribution, probability density ratio, and evaluation value related to certain one-axis inclination information in the first embodiment of the present invention. As shown in FIGS. 5A and 5B, when the peak of the probability density distribution is shifted, a distribution with a probability density ratio larger than 1 is generated around the shifted peak position. .

  Further, the first deterioration diagnosis unit 11 calculates the vector length by regarding the probability density ratio related to the slope calculated for each axis as a vector. Specifically, as shown in FIG. 5C, the first deterioration diagnosis unit 11 obtains a vector length as the Euclidean distance of the triaxial probability density ratio, and uses it as an evaluation value for deterioration diagnosis.

  Finally, in step S206, the first deterioration diagnosis unit 11 determines that the probability density ratio is normal if the vector length of the probability density ratio is less than the reference value for deterioration determination, and there is distribution data that exceeds the reference value. Determines that degradation has occurred.

When the above contents are arranged, the following processing is performed when the deterioration diagnosis of the attachment state of the structure is performed using the probability density distribution regarding the inclination information.
[Feature 1] The inclination information is acquired by extracting the low frequency component of the acceleration information.
[Characteristic 2] The sampling rate of the inclination information can be slowed so that the information is not lost. For example, data can be collected at intervals of 5 seconds.
[Characteristic 3] The tilt data whose attachment direction has been corrected is converted to decibels to reduce the storage capacity and perform efficient quantization.
[Feature 4] Based on a predetermined decibel unit, the probability density distribution at the time of learning and deterioration diagnosis is calculated, the vector length is obtained as the Euclidean distance of the triaxial probability density ratio, and used as an evaluation value for deterioration diagnosis. .
[Feature 5] If there is an evaluation value exceeding the reference value, it is determined that the tilt of the structure has reached an unacceptable level at the portion where the sensor head 20 is installed, and the deterioration state is determined quantitatively. .

(2) Flow of deterioration diagnosis processing based on natural frequency information by the second deterioration diagnosis unit 12 Next, specific processing contents by the second deterioration diagnosis unit 12 will be described.
FIG. 6 is a flowchart showing a flow of the deterioration diagnosis process by the second deterioration diagnosis unit 12 in the first embodiment of the present invention. A specific deterioration diagnosis process by the second deterioration diagnosis unit 12 based on the natural frequency information will be described based on the flowchart of FIG.

  First, in step S <b> 601, the second deterioration diagnosis unit 12 extracts frequency information based on acceleration information acquired from the acceleration information output unit 22 in the sensor head 20. For example, X, Y, and Z axis acceleration information is output from the sensor head 20 at a sampling rate of 50 Hz. However, large vibrations are not always generated. On the other hand, when performing frequency analysis, a high sampling rate is required, and a huge storage area is required to obtain data at all times.

  Therefore, the second deterioration diagnosis unit 12 according to the first embodiment, when the acceleration exceeding the preset reference level is obtained in at least one of the acquired three-axis acceleration information, To 2n constants (for example, 512) are acquired.

  FIG. 7 is a diagram showing a specific example related to collection of frequency information for three axes in the first embodiment of the present invention. In FIG. 7, when acceleration data measured on the X-axis exceeds a reference level, a certain number of data consisting of past data and future data is stored for each of the three axes, using that time as an event trigger. Is illustrated.

  Next, the second degradation diagnosis unit 12 corrects the mounting direction of the sensor head 20 in step S602. Specifically, the second deterioration diagnosis unit 12 performs a matrix of calibration data (affine transformation matrix elements) in the mounting direction of the sensor head 20 and a certain number of data obtained in the previous step S601. By calculating, the error in the mounting direction is corrected so that the coordinate axes of the respective sensor heads 20 coincide with each other. Here, when the sensor head 20 is replaced and a new sensor head is used, the calibration data must be replaced with data for the new sensor head.

Next, in step S603, the second deterioration diagnosis unit 12 calculates a power spectrum by FFT processing for the error-corrected three-axis data, and obtains a natural frequency. FIG. 8 is a diagram showing calculation results of power spectra for three axes in the first embodiment of the present invention. The acceleration data includes a direct current component (slope component). Therefore, the second deterioration diagnosis unit 12 removes the low-frequency component from the power spectrum, the maximum maximum value (corresponding to f _1x , f _1y , and f _1z in FIG. 8) from the remaining spectrum, and the second (Corresponding to f _2x , f _2y , and f _2z in FIG. 8) are obtained, and these are set as the first natural frequency and the second natural frequency of each axis, respectively.

  Next, in step S604, the second degradation diagnosis unit 12 creates a two-dimensional cumulative frequency distribution with the first natural frequency and the second natural frequency as orthogonal axes, and performs local smoothing processing on the distribution. As a result, a two-dimensional probability density distribution is generated. Specifically, the second deterioration diagnosis unit 12 is defined by, for example, the first natural frequency and the second natural frequency obtained in the previous step S603 for each event trigger in the previous step S601. A probability density distribution is generated by creating a two-dimensional cumulative frequency distribution and applying a local smoothing process to the cumulative frequency distribution. As the smoothing process, for example, a Gaussian function can be applied.

  The probability density distribution as the initial learning data at the time of installation is generated using, for example, data for about one month. On the other hand, the number of data of the probability density distribution created as current data at the time of deterioration diagnosis is generated based on data of the past 3 hours to 24 hours, for example.

  Further, it is not always necessary to continuously use the data acquired at the time of installation as the learning data at the normal time serving as the reference data. Since deterioration diagnosis is performed over a long period of time, it is possible to continuously update learning data in the process of performing deterioration diagnosis. The probability density distribution adopted as continuous learning data during deterioration monitoring is generated based on data of the past several hours to several weeks, for example.

  Next, in step S605, the second deterioration diagnosis unit 12 uses the two-dimensional probability density distribution of each axis (corresponding to the data at the time of diagnosis) obtained at the time of diagnosis as the two-dimensional probability density distribution of each axis obtained at the time of learning. By dividing by (equivalent to the reference data), the ratio between the two is calculated, and two-dimensional distribution data of the probability density ratio is generated for each axis. As reference data, learning data updated by continuous learning data can be adopted in addition to the data at the time of initial setting.

  FIG. 9 shows a two-dimensional probability density distribution at learning, a two-dimensional probability density distribution at the time of deterioration diagnosis, and a ratio of the two dimensions, calculated for the X axis as a certain axis in the first embodiment of the present invention. It is the figure which showed the specific example of the probability density ratio. As shown in FIG. 9, the probability density distribution related to the natural frequency is generated as two-dimensional data defined by the first natural frequency and the second natural frequency. This is the same as the idea of the one-dimensional probability density ratio regarding the inclination data by one deterioration diagnosis unit 11. The second deterioration diagnosis unit 12 uses a two-dimensional probability density ratio regarding two natural frequencies calculated for each axis as an evaluation value for deterioration diagnosis.

  Finally, in step S606, the second deterioration diagnosis unit 12 determines that the probability is higher if both of the triaxial probability density ratios are less than the deterioration determination reference value, and any one exceeds the reference value. When a two-dimensional distribution of density ratio exists, it is determined that deterioration has occurred.

When the above contents are arranged, the following processing is performed when the deterioration diagnosis of the mounting state of the structure is performed using the probability density distribution regarding the natural frequency information.
[Feature 1] With respect to acceleration information, natural frequency information is acquired by extracting a certain number of acceleration data including the point in time when acceleration data exceeding a reference level is obtained on any axis.
[Feature 2] Since the natural frequency information is used for subsequent frequency analysis, it is not appropriate to downsample the output rate from the sensor head 20. Therefore, when the acceleration data that exceeds the reference level in any axis is obtained as an event trigger, a certain number of acceleration data consisting of past data and future data is extracted and the natural frequency information is stored. The storage capacity is reduced.
[Characteristic 3] The power spectrum is calculated by performing frequency analysis on the natural frequency information in which the mounting direction is corrected, and the maximum maximum value and the second value are calculated from the remaining spectrum obtained by removing the low frequency component from the power spectrum. Is obtained, the first natural frequency and the second natural frequency of each axis are calculated.
[Feature 4] A two-dimensional probability at the time of learning and at the time of deterioration diagnosis from the cumulative frequency distribution of each axis created as a two-dimensional orthogonal coordinate by the first natural frequency and the second natural frequency calculated for each event trigger The density distribution is calculated, and the triaxial probability density ratio is used as an evaluation value for deterioration diagnosis.
[Feature 5] If there is an evaluation value exceeding the reference value, it is determined that the vibration of the structure has reached an unacceptable level at the portion where the sensor head 20 is installed, and the deterioration state is determined quantitatively. .

  As described above, according to the first embodiment, the first feature value related to the inclination and the second feature value related to the natural frequency are extracted based on the acceleration information obtained from the structure to be diagnosed for deterioration. Yes. For each feature, when a significant difference is detected by comparing the probability density distribution at the time of learning corresponding to the reference data at normal time and the probability density distribution based on the measurement result at the time of deterioration diagnosis, Judge that deterioration has occurred. As a result, the deterioration diagnosis of the mounting state of the structure can be performed quantitatively over a long period of time.

  In the above-described first embodiment, the deterioration diagnosis based on the first feature value related to the inclination and the deterioration diagnosis based on the second feature value related to the natural frequency have been described. It is possible to carry out the deterioration diagnosis quantitatively over a long period of time by performing only one.

  The deterioration diagnosis according to the first embodiment described above can be performed even when one sensor head is used as a minimum configuration. However, even if no deterioration has occurred at the location where the sensor head is installed, there is a possibility that the deterioration has occurred at other locations. Therefore, it is possible to expect improvement in detection accuracy by installing sensor heads at a plurality of locations and determining that the attachment deterioration of the structure has occurred when a deterioration state is detected by any of the sensor heads.

  Further, when the data of the two sensor heads can be used, in addition to the individual diagnosis results, the inclination is the difference between the inclinations of the two sensor heads, and the vibration frequency is the phase difference between the vibrations of the two sensor heads. As described above, deterioration diagnosis based on the probability density distribution can be performed, and further improvement in detection accuracy can be expected. In addition, regarding the vibration frequency, not only the phase difference but also the deterioration diagnosis can be performed similarly as the amplitude difference.

  Moreover, in Embodiment 1 mentioned above, the suspended structure in a tunnel or the overhang | projection structure was mentioned as an example of a degradation diagnosis object, However, This invention is not limited to this. If the structure is attached to the fixed surface and the attachment state may change over time, the deterioration diagnosis can be performed quantitatively over a long period of time. Further, by attaching a sensor head to an existing structure, it is possible to quantitatively diagnose a secular change in the mounting state of the structure after the installation of the sensor head.

  In the above-described first embodiment, the case of learning at the initial stage and the case of updating by learning at the time of deterioration diagnosis have been described as normal reference data. However, the present invention is not limited to this. Probability density distribution for judging normality does not need to be unique. For example, individual reference data is provided for each time zone, or separate reference data is provided for each event occurring in a structure. You can also. Furthermore, considering the secular change, it is also possible to use the probability density distribution at the initial stage and a new probability density distribution obtained by learning at the time of deterioration diagnosis.

Embodiment 2. FIG.
In the second embodiment, a function of performing abnormality (failure) diagnosis of sensor heads by comparing the detection results of a plurality of sensor heads 20 with each other will be described. In the second embodiment, “abnormality (failure) of the sensor head” refers to an installation abnormality that causes the sensor itself to drop or shift from the degradation diagnosis target structure due to some influence, and from the sensor. This is a concept including an electrical abnormality that makes it impossible to output an accurate detection value.

  As described in detail in the first embodiment, the present invention makes use of the acceleration information obtained from the deterioration diagnosis target structure using the sensor head 20 to perform the deterioration diagnosis of the attachment state of the structure. Realized with high accuracy. Here, in order to ensure the reliability of the deterioration diagnosis, it is important to ensure the reliability of the acceleration information itself detected by the sensor head 20.

  Therefore, in the second embodiment, the failure detection of the sensor head itself is performed separately from the deterioration diagnosis by comparing the detection results of the plurality of sensor heads 20 with each other. In order to simplify the description, a method for diagnosing a failure of the sensor head by comparing the detection results of the two sensor heads 20 (1) and 20 (2) will be described below.

  In the first embodiment, in order to perform the deterioration diagnosis, the probability density ratio as a ratio of the probability density distribution at the time of learning and the probability density distribution at the time of deterioration diagnosis is calculated. On the other hand, in the second embodiment, in order to perform failure diagnosis of the sensor head itself, as a ratio of probability density distributions of the plurality of sensor heads 20 (1) and 20 (2) at the time of deterioration diagnosis, Probability density ratio is calculated.

  Specifically, as described in the first embodiment, the sensor controller 10 already has the X-axis and Y-axis for the sensor head 20 (1) and the sensor head 20 (2) at the time of deterioration diagnosis. The probability density distribution of each axis of the Z axis has been calculated. Therefore, the sensor controller 10 calculates the probability density ratio of the two sensor heads 20 (1) and 20 (2) for each axis, and determines whether or not the calculated probability density ratio is within a predetermined allowable range. Judging.

  Finally, when the probability density ratio of at least one of the axes deviates from the allowable range, the sensor controller 10 has two sensor heads 20 (1), 20 (2) due to failure or dropout. It can be determined that an abnormality has occurred in at least one of the above. As the probability density distribution in such failure diagnosis, either the probability density distribution based on the slope information described in the first embodiment or the probability density distribution based on the frequency information can be adopted. . In that case, the sensor controller 10 determines that no failure has occurred when both of the failure diagnosis results based on both the inclination information and the frequency information are normal (when all probability density ratios are within an allowable range). You can also

  As described above, according to the second embodiment, the ratio of probability density distributions based on the detection results of a plurality of sensor heads at the time of deterioration diagnosis deviates from an allowable range, thereby enabling failure diagnosis of the sensor head itself. Yes. As a result, it is possible to ensure the reliability of the deterioration diagnosis after confirming the soundness of the sensor head.

  Note that the above-described failure diagnosis need not always be performed at the same timing as the deterioration diagnosis. For example, failure diagnosis can be performed periodically to confirm that there is no abnormality in the sensor head itself when a deterioration state is detected by the deterioration diagnosis.

  In addition, as the second sensor head for performing the failure diagnosis of the first sensor head, the sensor head installed for performing the deterioration diagnosis can also be diverted. It can also be installed in the vicinity of the sensor head.

  In the second embodiment described above, the case where the failure diagnosis of the sensor is performed using the probability density distribution and the probability density ratio used in the deterioration diagnosis of the structure has been described. However, the present invention is not limited to this. It is not something. When performing sensor failure diagnosis, historical data (or machining data) obtained from acceleration information, which is the output of the sensor head, is used instead of the probability density distribution, and historical data comparison values are used instead of the probability density ratio. It is also possible to adopt.

Embodiment 3 FIG.
In the third embodiment, when the sensor head 20 is replaced due to a failure or a lifespan, the detection data by the new sensor head is used so that the learning data collected by the old sensor head can be used to continue the monitoring state. A method for calibrating the camera will be described. In order to simplify the description, an explanation will be given below by taking as an example a calibration when replacing the old sensor head 20old installed at a certain place with a new sensor head 20new installed in the vicinity thereof.

  In the third embodiment, calibration is performed as follows by providing a period in which the new sensor head 20new is provided near the old sensor head 20old. When replacing the old sensor head 20old with the new sensor head 20new, the X, Y, and Z axis tilts axold, ayold, and azold detected by the old sensor head 20old, and X detected by the new sensor head 20new, The monitoring state can be continued by aligning the tilts axnew, aynew, and aznew of Y and Z (that is, by matching the coordinate axes of the old sensor head 20old and the new sensor head 20new). .

  Therefore, the sensor controller 10 uses the three-axis inclinations axold, ayold, and azold detected by the old sensor head 20old and the three-axis inclinations axnew, aynew, and aznew detected by the new sensor head 20new in the side-by-side period. Thus, an affine transformation matrix X satisfying the relationship of the following expression (1) is obtained.

  After the affine transformation matrix X can be calculated in this way, the sensor controller 10 determines the three-axis inclinations a (xnew), a (ynew), a (znew) detected by the new sensor head 20new, and the affine By performing the calculation of the above equation (1) using the transformation matrix X, it is possible to obtain data in which the coordinate axis coincides with the data obtained in the old sensor head 20old by the new sensor head 20new.

  In the above equation (1), the case where the transformation matrix is calculated based on the inclination information detected by each of the old sensor head 20old and the new sensor head 20new has been described. However, the present invention is not limited to this, and in calculating the transformation matrix, it is possible to use a detection result that changes according to the installation state of the sensor head, and use natural frequency information instead of inclination information. It is also possible to calculate a transformation matrix based on this.

  The transformation using the affine transformation matrix may be applied to the data of the old sensor head 20old without being applied to the detection result of the new sensor head 20new, that is, the learning data collected by the old sensor head 20old is reversed. It may be converted and processed to match the new sensor head.

  Further, the calibration function described in step S202 of FIG. 2 in the first embodiment corrects so that the coordinate axis always coincides with a reference direction such as the direction of gravity without depending on the mounting position of the sensor head 20. Is.

  Therefore, the calibration function in the first embodiment is adapted to the reference coordinate axis instead of the coordinate axis of the old sensor head 20old, and the concept of calibration is the same as that of the third embodiment. is there.

  That is, in the third embodiment, for example, when the reference information (corresponding to the reference inclination information or the reference natural frequency information) when the gravity direction is set as reference is known, the current sensor head 20 is used. By substituting the detection information and this reference information into the above equation (1), a conversion matrix can be calculated.

  Then, each time the sensor head 20 is replaced, such a conversion matrix is obtained, and the sensor controller 10 converts the detection information by the sensor head 20 using the conversion matrix, so that the sensor controller 10 can move in the reference gravity direction. It is possible to always calculate the normalized feature amount based on the matched information without depending on the sensor mounting direction.

  In this way, by performing data conversion using the conversion matrix, even after the old sensor head 20old is removed, the learning data accumulated so far based on the detection result by the old sensor head 20old is not exchanged. The data of other sensor heads and the permissible level used for deterioration determination can be used as they are, and the monitoring state can be continued while maintaining a constant deterioration diagnosis performance.

  As described above, according to the third embodiment, when exchanging sensor heads, a period in which two new and old sensor heads are provided is provided, and an affine transformation matrix is calculated from detection results of both sensor heads. Alternatively, when the sensor head is replaced, an affine transformation matrix is calculated so that a detection result that coincides with a reference direction such as the direction of gravity is obtained. As a result, by applying affine transformation, it is possible to effectively utilize the data obtained in the sensor head before replacement, suppressing the influence on the degradation diagnosis performance due to the difference in the mounting direction accompanying the replacement of the sensor head, It is possible to maintain a constant deterioration diagnosis performance and continue the monitoring state.

  DESCRIPTION OF SYMBOLS 10 Sensor controller, 11 1st degradation diagnostic part, 12 2nd degradation diagnostic part, 20 Sensor head, 21 Sensor part, 22 Acceleration information output part

Claims (2)

Diagnose whether the mounting state of the structure is normal or not based on the acceleration information of the structure output from each of a plurality of sensor heads mounted on the structure that is attached to the fixed surface and subject to deterioration diagnosis In the structure deterioration diagnosis system provided with the sensor controller, the structure deterioration diagnosis system executed by the sensor controller,
A feature of individually extracting at least one of the tilt information and the natural frequency information of the structure individually for each of the plurality of sensor heads as a feature amount based on the acceleration information acquired from each of the plurality of sensor heads. A quantity extraction unit;
When the reference data obtained in the normal state and the diagnostic data obtained at the time of deterioration diagnosis have a significant difference of a predetermined amount or more based on the feature quantity extracted by the feature quantity extraction unit, the structure And a determination unit that determines that an abnormality has occurred in the mounting state of
The determination unit
At the time of deterioration diagnosis, in order to determine that the plurality of sensor heads are not in an abnormal state, individually calculate sensor diagnosis history data obtained from the acceleration information,
Of the plurality of sensor heads, the comparison is a ratio of the first sensor diagnosis history data calculated for the first sensor head and the second sensor diagnosis history data calculated for the second sensor head. A value is calculated, and when the calculated comparison value is not within a predetermined allowable range, it is determined that at least one of the first sensor head and the second sensor head is in an abnormal state. Deterioration diagnosis system. In the structure deterioration diagnosis system according to claim 1,
The determination unit
As the sensor diagnosis history data, diverting the diagnostic data individually calculated for the plurality of sensor heads for deterioration diagnosis,
Of the plurality of sensor heads, the comparison value is calculated as a ratio between the first diagnosis data calculated for the first sensor head and the second diagnosis data calculated for the second sensor head, and is calculated If the comparison value is not within a predetermined allowable range, it is determined that at least one of the first sensor head and the second sensor head is in an abnormal state.

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