Sensor adhesion state determination device, sensor adhesion state determination system, and sensor adhesion state determination method

Abstract

To provide a sensor adhesion state determination device, a sensor adhesion state determination system, and a sensor adhesion state determination method capable of accurately determining an adhesion state of a sensor.SOLUTION: A sensor adhesion state determination device according to an embodiment includes a signal processing unit, an index value acquisition unit, and a determination unit. The signal processing unit acquires intensity information on signal intensities in different frequency bands of an elastic wave detected by one or more sensors that detect the elastic wave generated by an impact applied by an impact application unit that applies the impact to a structure. The determination unit determines the adhesion state of the one or more sensors on the basis of the strength information acquired by the signal processing unit.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a sensor adhesion state determination device, a sensor adhesion state determination system, and a sensor adhesion state determination method. [Background technology]

[0002] In recent years, problems associated with the deterioration of infrastructure structures such as bridges have become apparent. This has led to an increasing need for non-destructive testing, and attention has been drawn to the AE (Acoustic Emission) method, which detects internal damage in structures by detecting elastic waves generated by damage using sensors with high sensitivity and high frequency band sensitivity (e.g., AE (Acoustic Emission) sensors). Conventionally, methods have been proposed in which the location of elastic wave sources (hereinafter referred to as "elastic wave sources") is identified, and the presence or absence of internal damage in a structure is diagnosed on a planar basis using the elastic wave source density distribution based on the identified multiple elastic wave sources.

[0003] Sensors used in such nondestructive testing are attached to the surface of the structure to be measured directly with adhesive or indirectly via a jig. However, poor adhesion can occur due to poor adhesion work or changes over time. When adhesion is poor, elastic waves are difficult to transmit to the sensor, resulting in signal changes. As a result, this change is difficult to distinguish from signal changes caused by deterioration of the structure, leading to reduced diagnostic accuracy and misdiagnosis. Therefore, when installing the sensor, elastic waves are generated near the sensor and the adhesion condition is determined based on the amplitude value when the elastic waves are detected by the sensor. For example, Patent Document 1 discloses a method in which elastic waves excited by applying pulses to a piezoelectric element are detected by the sensor and the adhesion condition is determined based on the peak frequency of the detected elastic waves.

[0004] However, with this method, even if the sensor is not well attached, the amplitude of the detected elastic waves excited near the sensor may be large enough to cause errors in the judgment of the sensor's adhesiveness.Furthermore, when using the peak frequency, even a slight difference in amplitude may cause a large deviation in the peak frequency, resulting in errors in the judgment of the sensor's adhesiveness. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-155661 Summary of the Invention [Problem to be solved by the invention]

[0006] The problem to be solved by the present invention is to provide a sensor adhesion state determination device, a sensor adhesion state determination system, and a sensor adhesion state determination method that can accurately determine the adhesion state of a sensor. [Means for solving the problem]

[0007] The sensor adhesion state determination device according to the embodiment includes a signal processing unit, an index value acquisition unit, and a determination unit. The signal processing unit acquires intensity information relating to signal intensities in different frequency bands of elastic waves detected by one or more sensors that detect elastic waves generated by an impact applied by an impact application unit that applies an impact to a structure. The determination unit determines the adhesion state of the one or more sensors based on the intensity information acquired by the signal processing unit. [Brief explanation of the drawings]

[0008] [Figure 1] 10A and 10B are diagrams for explaining an outline of a conventional method for determining the adhesion state of a sensor. [Figure 2] FIG. 1 is a diagram for explaining the surface roughness of a structure. [Figure 3] 10A and 10B are diagrams for explaining differences in elastic waves detected depending on the installation state of the sensor. [Figure 4] FIG. 2 is a diagram for explaining a propagation path of an elastic wave. [Figure 5] 1 is an explanatory diagram of surface waves and body waves. [Figure 6] FIG. 1 is a diagram showing an example of a system configuration of a sensor adhesion state determination system according to an embodiment. [Figure 7] 4 is a flowchart showing a flow of processing performed by the sensor adhesion state determination system in the embodiment. [Figure 8] 10A to 10C are diagrams showing results of applying a technique according to an embodiment. [Figure 9] 4 is a flowchart showing a flow of a process for improving the adhesion state in the sensor adhesion state determination system according to the embodiment. [Figure 10] 10A to 10C are diagrams for explaining the effects of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A sensor adhesion state determination device, a sensor adhesion state determination system, and a sensor adhesion state determination method according to embodiments will be described below with reference to the accompanying drawings.

[0010] (Explanation of the principle) Before describing the details of the embodiment, an outline of a conventional sensor adhesion state determination will be described. Fig. 1 is a diagram for explaining the outline of a conventional sensor adhesion state determination. Fig. 1 shows a case where a sensor S is attached to a structure TS via a couplant C. For example, Fig. 1(A) shows a case where no adhesion failure, including voids, occurs on the adhesion surface between the sensor S and the structure TS, and Fig. 1(B) shows a case where adhesion failure, including voids, occurs on the adhesion surface between the sensor S and the structure TS.

[0011] In the state shown in Figure 1(A), the elastic waves generated by the pencil lead break (PLB) method are transmitted to the sensor S and detected with a sufficiently large amplitude. On the other hand, in the state shown in Figure 1(B), the elastic waves are attenuated and the detected amplitude becomes smaller. Conventionally, it has been said that the adhesive state of the sensor S can be determined based on the difference in amplitude.

[0012] However, as shown in Figure 2, if the surface roughness of the structure TS is large, the gel-like couplant C fills in the surface roughness. As a result, although there are no voids, the thickness d of the couplant C becomes large. In such cases, it is known that elastic waves excited by a PLB or other device nearby can be detected with sufficient amplitude. In other words, differences in the couplant thickness d cannot be distinguished by the amplitude of the elastic waves generated by the axial compression method.

[0013] Figure 3 shows the results of measuring the waveforms of elastic waves generated by the axial compression method on three test specimens: one with a good bond as shown in Figure 1(A), one with a void as shown in Figure 1(B), and one filled with a thick adhesive (e.g., grease) as shown in Figure 2. Figure 3(A) shows the waveform of an elastic wave measured in a good bond state, Figure 3(B) shows the waveform of an elastic wave measured in a void state, and Figure 3(C) shows the waveform of an elastic wave measured in a void-filled state. Figure 3(D) shows the spectral waveform of an elastic wave measured in a good bond state, Figure 3(E) shows the spectral waveform of an elastic wave measured in a void-filled state, and Figure 3(F) shows the spectral waveform of an elastic wave measured in a void-filled state.

[0014] As shown in Figure 3(B), when there is a gap, the amplitude is reduced compared to other conditions, and when it is filled with a thick adhesive, the amplitude is improved, as shown in Figure 3(C).Comparing the spectra of the elastic wave waveforms shown in Figure 3(A) and 3(C), it can be seen that when the adhesive is well bonded, the frequency characteristics are concentrated in the low frequency region around 20 kHz, as shown in Figure 3(D), whereas when it is filled with a thick adhesive (for example, grease), the frequency characteristics are relatively flat, as shown in Figure 3(F).

[0015] The reasons for the above can be explained using Figures 4 and 5. Figure 4 is a diagram illustrating the propagation path of elastic waves. As shown in Figure 4, elastic waves excited by the PLB consist of two types: a surface wave W1 that propagates along the surface of the structure TS, and a body wave W2 that propagates within the structure TS, reflects, and reaches the sensor S. The surface wave W1 reaches the sensor S first. The surface wave W1 is a Rayleigh wave that accompanies out-of-plane vibrations on the surface of the structure TS, and is transmitted to the sensor S via the couplant C. On the other hand, the body wave W2 that is incident approximately perpendicular to the sensor S consists of a longitudinal wave (p wave) and a shear wave (s wave). The shear wave, which has a large amplitude, vibrates perpendicular to the direction of propagation and vibrates in the in-plane direction on the surface of the structure TS.

[0016] FIG. 5 is an explanatory diagram of the surface wave W1 and the body wave W2. As shown in FIG. 5(A), the surface wave W1 is a direct wave and is detected with sufficient amplitude by the sensor S. The couplant C, which is typically made of gel or liquid, becomes significantly more difficult to propagate shear waves through as its thickness increases. Therefore, as shown in FIG. 5(B), the amplitude of the body wave W2 reaching the sensor S is attenuated. When the couplant C is thick, the surface wave, which is a direct wave, is detected with sufficient amplitude, while the body wave reflected from the opposite surface of the structure is attenuated relatively more. Because the peak amplitude is determined by the direct surface wave, it does not change significantly even if the thickness of the couplant C increases. However, actual elastic waves are primarily body waves, which are significantly attenuated by the thickness of the couplant C.

[0017] Here, we focus on the frequency of the body wave. If the thickness T of the structure TS is sufficiently greater than the distance L between the elastic wave source (here, the position of the PLB) and the sensor S, the propagation distance of the body wave is longer than that of the surface wave. As a result, the high-frequency components are affected by scattering and attenuation, and are reduced by the effect of a low-pass filter. If the sensor S is well bonded and the body wave can be detected, the low-frequency components of the body wave will be added to the direct wave. If the sensor S is poorly bonded and the propagation of the body wave is obstructed, the high-frequency components of the direct wave will become relatively larger. This phenomenon is also supported by the results in Figure 3. In other words, it is possible to determine the bonding condition of the sensor S based on the magnitude relationship between at least the low-frequency and high-frequency components of the frequency spectrum.

[0018] The low-frequency components referred to here are components near the cutoff frequency of the high-pass filter in existing AE measurement devices. Typically, the cutoff frequency of the high-pass filter is set to be equal to or lower than the resonant frequency fr of the sensor S, preferably fr / 2 or lower. The high-frequency components are the high-frequency components of the elastic waves excited by the PLB, and are usually in the range of 100 kHz to 1 MHz. Taking the case where the sensor resonant frequency is 50 kHz as an example, the low-frequency components can be set to be components near 20 kHz, and the high-frequency components can be set to be components near 100 kHz.

[0019] Therefore, in this embodiment, the adhesive state of the sensor is determined based on information in a plurality of different frequency bands of elastic waves, rather than the peak frequency. A specific method will be described below with reference to the embodiment.

[0020] (Embodiment) FIG. 6 is a diagram showing an example of the system configuration of a sensor adhesion state determination system 100 according to an embodiment. The sensor adhesion state determination system 100 is a system for determining the adhesion state of a sensor adhered to a structure. In this embodiment, a bridge will be described as an example of a structure, but the structure does not have to be limited to a bridge. For example, the structure may be any structure that generates elastic waves due to the occurrence or progression of a crack or an external impact (for example, rain, artificial rain, etc.). Note that bridges are not limited to structures built over rivers or valleys, but also include various structures built above ground level (for example, highway viaducts). The structure may be a plate-shaped member.

[0021] Damage that affects the assessment of the deterioration state of the structure 50 includes damage inside the structure that interferes with the propagation of elastic waves, such as cracks, cavities, and sedimentation. Here, cracks include vertical cracks, horizontal cracks, and diagonal cracks. Vertical cracks are cracks that occur in a direction perpendicular to the road surface. Horizontal cracks are cracks that occur horizontally to the road surface. Diagonal cracks are cracks that occur in a direction other than horizontal or vertical to the road surface. Sedimentation is deterioration in which concrete turns into sediment, mainly at the boundary between the asphalt and the concrete deck.

[0022] The sensor adhesive state determination system 100 includes an impact applying unit 10, a sensor 20, and an adhesive state determination device 25. The sensor 20 and the adhesive state determination device 25 are connected to each other so that they can communicate with each other via wire or wirelessly. An amplifier circuit such as a preamplifier or a noise filter circuit for removing noise may be provided between the sensor 20 and the adhesive state determination device 25, as necessary. Although FIG. 6 shows a case where one sensor 20 is provided, multiple sensors 20 may be provided.

[0023] The impact applying unit 10 applies an impact to the structure 50. Applying an impact to the structure 50 means, in other words, exciting an elastic wave. The method by which the impact applying unit 10 applies an impact (elastic wave excitation means) is preferably a method that can generate an elastic wave containing a frequency component higher than the resonant frequency of the sensor 20, and methods such as the PLB method, steel ball striking, water droplet spraying, or pulse excitation using a piezoelectric element may be used. Note that this is just one example, and the method by which the impact applying unit 10 applies an impact (elastic wave excitation means) may be any method that can generate an elastic wave containing a frequency component higher than the resonant frequency of the sensor 20, as described above.

[0024] The impact applying unit 10 applies an impact to the structure 50, for example, in the vicinity of the sensor 20 that is the target of bond state determination. The sensor 20 that is the target of bond state determination is at least one sensor 20 installed in the structure 50. Furthermore, the vicinity of the sensor 20 is preferably less than one wavelength of the excited elastic wave. As a representative example, if the resonant frequency of the sensor 20 is 50 kHz and the speed at which elastic waves propagate through the structure 50 (e.g., concrete) is 3000 m / s, then the target distance can be within 60 mm.

[0025] The sensor 20 is adhered to the surface of the structure 50 that is the target of deterioration evaluation using an adhesive such as grease or couplant. The surface of the structure 50 on which the sensor 20 is installed may be the running surface on which vehicles and the like run, the surface opposite the running surface (the bottom surface of the structure), or a side surface of the structure 50. The sensor 20 has a piezoelectric element and detects elastic waves generated by the impact applied by the impact application unit 10. The sensor 20 converts the detected elastic waves into an analog electrical signal (hereinafter referred to as an "analog signal") and outputs it to the adhesion condition determination device 25. In the following explanation, an example will be described in which the sensor 20 is installed on the bottom surface of the structure 50.

[0026] The sensor 20 uses a piezoelectric element having sensitivity in the range of, for example, 10 kHz to 1 MHz. There are various types of sensors 20, such as a resonance type that has a resonance peak within a frequency range and a wideband type that suppresses resonance, but any type of sensor 20 may be used. The method by which the sensor 20 detects elastic waves includes a voltage output type, a resistance change type, and a capacitance type, but any detection method may be used. The sensor 20 is, for example, an AE sensor.

[0027] Note that the sensor 20 is not limited to an AE sensor, and other sensors may be used. For example, an acceleration sensor may be used instead of the sensor 20. In this case, the acceleration sensor detects elastic waves generated inside the structure 50. Then, the acceleration sensor converts the detected elastic waves into analog signals by performing the same processing as the sensor 20.

[0028] The adhesive state determination device 25 determines the adhesive state of the sensor 20 based on the elastic waves detected by the sensor 20. The adhesive state determination device 25 includes an AD converter 250, a signal processing unit 251, an index value acquisition unit 252, a determination unit 253, an output control unit 254, and an output unit 255.

[0029] The AD converter 250 receives an analog signal output from the sensor 20 as input. The AD converter 250 performs analog-to-digital conversion on the input analog signal. As a result, the AD converter 250 converts the input analog signal into a digital signal. The AD converter 250 outputs the converted digital signal to the signal processing unit 251.

[0030] The signal processing unit 251 is configured using a digital circuit. The digital circuit is realized by, for example, an FPGA (Field Programmable Gate Array) or a microcomputer. The digital circuit may also be realized by a dedicated LSI (Large-Scale Integration). The signal processing unit 251 may also be equipped with a non-volatile memory such as a flash memory or a removable memory. The signal processing unit 251 receives as input the digital signal output from the AD converter 250. The signal processing unit 251 performs signal processing on the input digital signal.

[0031] The signal processing unit 251 extracts a signal waveform corresponding to an elastic wave based on a trigger signal indicating that the amplitude value of the digital signal has exceeded a predetermined threshold value. Next, the signal processing unit 251 converts the signal waveform in the time domain into a frequency spectrum, which is a signal waveform in the frequency domain, by performing a fast Fourier transform (FFT) on the signal waveform.

[0032] The signal processing unit 251 uses the frequency spectrum to acquire intensity information regarding the signal intensity in different frequency bands of the elastic wave. Here, the different frequency bands are, for example, two or more frequency bands, such as a frequency band including low frequency components (hereinafter referred to as "low frequency band") and a frequency band including high frequency components (hereinafter referred to as "high frequency band"). The low frequency band corresponds to the first frequency band in the claims, and the high frequency band corresponds to the second frequency band in the claims.

[0033] In the following description, an example will be described in which the low frequency band is in the range of 20 kHz to 40 kHz and the high frequency band is in the range of 90 kHz to 110 kHz. The ranges of the low frequency band and the high frequency band are not limited to the above example and may be set as appropriate. Preferably, the low frequency band is in the range of around 20 kHz, and the high frequency band is in the range of around 100 kHz. The signal processing unit 251 acquires intensity information in the low frequency band by setting the lowest frequency of the different frequency bands to a frequency lower than the resonant frequency of the sensor 20. Furthermore, the signal processing unit 251 acquires intensity information in the high frequency band by setting the highest frequency of the different frequency bands to a frequency higher than the resonant frequency of the sensor 20.

[0034] Furthermore, the intensity information regarding the signal intensity is, for example, a statistical value of amplitude (for example, an average value, a maximum value, etc.) or energy. In this embodiment, a case will be described in which the signal processing unit 251 acquires intensity information regarding the signal intensity in each of the low frequency band and the high frequency band.

[0035] For example, the signal processing unit 251 uses the converted frequency spectrum to calculate the average amplitude in a low frequency band (for example, 20 kHz to 40 kHz) and the average amplitude in a high frequency band (for example, 90 kHz to 110 kHz). For example, the signal processing unit 251 uses the converted frequency spectrum to calculate the maximum value in the low frequency band (for example, 20 kHz to 40 kHz) and the maximum value in the high frequency band (for example, 90 kHz to 110 kHz). For example, the signal processing unit 251 uses the converted frequency spectrum to calculate the energy in the low frequency band (for example, 20 kHz to 40 kHz) and the energy in the high frequency band (for example, 90 kHz to 110 kHz).

[0036] The index value acquiring unit 252 acquires an index value for determining the adhesive state of each sensor 20 based on the intensity information acquired by the signal processing unit 251. The index value is a numerical value indicating the quality of the adhesive state of the sensor 20, and for example, a larger numerical value indicates a better adhesive state and a smaller numerical value indicates a poorer adhesive state. The index value acquiring unit 252 acquires, as the index value, the ratio value of the value indicated by the intensity information in the low frequency band to the value indicated by the intensity information in the high frequency band (value indicated by the intensity information in the low frequency band / value indicated by the intensity information in the high frequency band) or the difference value (value indicated by the intensity information in the low frequency band-value indicated by the intensity information in the high frequency band).

[0037] When the intensity information is a statistical value of amplitude, the index value acquiring unit 252 acquires, as an index value, a ratio or a difference between a statistical value of amplitude in a low frequency band and a statistical value of amplitude in a high frequency band.When the intensity information is energy, the index value acquiring unit 252 acquires, as an index value, a ratio or a difference between, for example, energy in a low frequency band and energy in a high frequency band.

[0038] The determination unit 253 determines the adhesion state of each sensor 20 based on the index value acquired by the index value acquisition unit 252. Specifically, the determination unit 253 determines that the adhesion state of a sensor 20 whose index value is equal to or less than a predetermined determination threshold is poor. When the index value is acquired based on a ratio value, the determination threshold is, for example, 1. When the index value is acquired based on a difference value, the determination threshold is, for example, 0.

[0039] Here, a poor adhesion state refers to, for example, a case where there is a gap or the like on the adhesive surface and part of the sensor 20 is not adhered to the structure 50, or a case where the adhesive is too thick and may reduce the detection accuracy of the elastic wave, etc. The determination unit 253 determines that the adhesion state of the sensor 20 is good when the index value is greater than a predetermined determination threshold value.

[0040] The output control unit 254 causes the output unit 255 to output information based on the determination result by the determination unit 253. For example, when the determination result by the determination unit 253 indicates that the adhesive state of the sensor 20 is good, the output control unit 254 causes the output unit 255 to output information indicating that the adhesive state of the sensor 20 is good. For example, when the determination result by the determination unit 253 indicates that the adhesive state of the sensor 20 is poor, the output control unit 254 causes the output unit 255 to output, together with the identification information of the sensor 20, information urging the user to polish the surface of the structure 50.

[0041] In addition, if the judgment result by the judgment unit 253 for the same sensor 20 indicates that the adhesion state of the sensor 20 is poor a predetermined number of times (for example, twice) in succession, the output control unit 254 causes the output unit 255 to output information urging the user to change the installation position of the sensor 20.

[0042] The output unit 255 outputs information based on the determination result by the determination unit 253 in a predetermined manner under the control of the output control unit 254. The output unit 255 may display the information on a screen or may print the information on a medium and output it.

[0043] Fig. 7 is a flowchart showing the flow of processing performed by the sensor adhesion state determination system 100 in this embodiment. Fig. 7 illustrates an example in which amplitude statistics are acquired as intensity information, but the processing is similar when energy is acquired as intensity information. Furthermore, when multiple sensors 20 are installed, the processing shown in Fig. 7 can be performed for each sensor 20.

[0044] In response to a user's operation, the impact applying unit 10 applies an impact to the structure 50 near the sensor 20, which is to be subjected to bond state determination (step S101). The impact applied by the impact applying unit 10 generates an elastic wave. The sensor 20 detects the generated elastic wave (step S102). The sensor 20 converts the detected elastic wave into an analog signal and outputs it to the bond state determination device 25. The AD converter 250 included in the bond state determination device 25 converts the input analog signal into a digital signal. The AD converter 250 outputs the converted digital signal to the signal processing unit 251.

[0045] The signal processing unit 251 extracts a signal waveform corresponding to an elastic wave based on a trigger signal indicating that the amplitude value of the digital signal output from the AD converter 250 has exceeded a predetermined threshold. The signal processing unit 251 acquires a frequency spectrum by performing a fast Fourier transform on the extracted signal waveform (step S103). The signal processing unit 251 uses the acquired frequency spectrum to acquire statistical values ​​of amplitude in a low-frequency band (step S104). For example, the signal processing unit 251 acquires amplitude values ​​within the low-frequency band of the acquired frequency spectrum, and calculates the average value of the amplitude values ​​within the acquired low-frequency band or selects the maximum value to acquire statistical values ​​of amplitude in the low-frequency band.

[0046] The signal processing unit 251 acquires amplitude statistics in the high frequency band using the acquired frequency spectrum (step S105). For example, the signal processing unit 251 acquires amplitude values ​​within the high frequency band of the acquired frequency spectrum, and calculates the average value of the amplitude values ​​within the acquired high frequency band, or selects the maximum value, thereby acquiring the amplitude statistics in the high frequency band. The signal processing unit 251 outputs the acquired amplitude statistics in the low frequency band and the amplitude statistics in the high frequency band to the index value acquisition unit 252.

[0047] The index value acquiring unit 252 acquires an index value based on the statistical value of the amplitude in the low frequency band and the statistical value of the amplitude in the high frequency band output from the signal processing unit 251 (step S106). For example, the index value acquiring unit 252 acquires a ratio or difference between the statistical value of the amplitude in the low frequency band and the statistical value of the amplitude in the high frequency band as an index value. The index value acquiring unit 252 outputs the acquired index value to the determining unit 253.

[0048] The determination unit 253 compares the index value output from the index value acquisition unit 252 with a determination threshold. The determination unit 253 determines whether the index value is greater than the determination threshold (step S107). If the determination unit 253 determines that the index value is greater than the determination threshold, the determination unit 253 determines that the adhesive state of the sensor 20 from which the elastic wave was acquired is good (step S108). The determination unit 253 outputs a determination result indicating that the adhesive state of the sensor 20 is good to the output control unit 254.

[0049] On the other hand, if the determination unit 253 determines that the index value is equal to or less than the determination threshold, the determination unit 253 determines that the adhesive state of the sensor 20 from which the elastic wave is acquired is poor (step S109). The determination unit 253 outputs a determination result indicating that the adhesive state of the sensor 20 is poor to the output control unit 254.

[0050] (Application example) An example in which the above method was applied to an actual bridge is shown in Fig. 8. Fig. 8 is a diagram showing the results of applying the method in the embodiment. In the experiment on the actual bridge, 18 sensors 20 were installed on a structure 50. As shown in Fig. 8, of the 18 sensors 20, the index values ​​of four sensors corresponding to Ch2, Ch10, Ch13, and Ch14 were negative. Therefore, the bonding condition determination device 25 determines that the four sensors 20 corresponding to Ch2, Ch10, Ch13, and Ch14 with negative index values ​​have poor bonding.

[0051] Fig. 9 is a flowchart showing the flow of processing for improving the adhesive state in the sensor adhesive state determination system 100 according to the embodiment. The processing in Fig. 9 includes some manual processing. In addition, when multiple sensors 20 are installed, the processing shown in Fig. 9 may be executed for each sensor 20.

[0052] The user installs a holder for installing the sensor 20 on the structure 50 (step S201). The user wires a cable to the installed holder (step S202). The user installs the sensor 20 in the installed holder (step S203). After that, the number of times of implementation is initialized (i=0) (step S204). The impact applying unit 10 applies an impact to the structure 50 near the sensor 20, which is the target of adhesive state determination, in response to the user's operation (step S205). The sensor 20 detects the generated elastic waves, converts the detected elastic waves into analog signals, and outputs them to the adhesive state determination device 25. The AD converter 250 included in the adhesive state determination device 25 converts the input analog signals into digital signals. The AD converter 250 outputs the converted digital signals to the signal processing unit 251.

[0053] The signal processing unit 251 determines whether the amplitude value of the digital signal output from the AD converter 250 is within a reference value (step S206). This process is performed to check whether the sensor 20 is correctly detecting the signal before determining the adhesive state of the sensor 20. The reference value may be, for example, equal to or less than a predetermined threshold value that triggers elastic wave detection. If the signal processing unit 251 determines that the amplitude value of the digital signal is within the reference value (step S206-YES), the adhesive state determination device 25 performs adhesive state determination processing (step S207). The adhesive state determination processing is the processing from step S102 onwards shown in FIG. 7.

[0054] The user determines whether the results output from the output unit 255 in the adhesive state determination process indicate good adhesion (step S208). If the results output from the output unit 255 indicate good adhesion (step S208-YES), the user starts AE measurement of the structure 50 (step S209). On the other hand, if the results output from the output unit 255 do not indicate good adhesion (step S208-NO), the user determines whether the number of times i is performed is less than a specified number of times (step S210).

[0055] If the number of times i is less than the designated number of times (step S210-YES), the user polishes the surface of the structure 50 on which the sensor 20 is installed (step S211). After that, the user adjusts the installation position of the sensor 20 (step S212). For example, after step S211, the user places the sensor 20 again on the polished surface of the structure 50. After that, the user adds 1 to the number of times i and executes the process of step S205.

[0056] If the signal processing unit 251 determines in the process of step S206 that the amplitude value of the digital signal is not within the reference value (step S206-NO), or if the number of times i is performed is equal to or greater than the designated number of times (step S210-NO), it is assumed that the installation position of the sensor 20 is poor. Therefore, the user adjusts the installation position of the sensor 20 (step S212). In this case, the user places the sensor 20 in a location different from where the sensor 20 was originally installed. Thereafter, the user adds 1 to the number of times i is performed, and executes the process of step S205.

[0057] According to the sensor adhesive state determination system 100 configured as described above, the adhesive state determination device 25 includes a signal processing unit 251 that acquires intensity information in different frequency bands of elastic waves detected by one or more sensors 20 that detect elastic waves generated by an impact applied by an impact application unit 10 that applies an impact to a structure 50, and a determination unit 253 that determines the adhesive state of the one or more sensors 20 based on the intensity information acquired by the signal processing unit 251.

[0058] Figure 10 shows an example of the actual improvement in the adhesive condition of the sensor 20 achieved by the sensor adhesive condition determination system 100 configured as described above. The reference value for determining whether or not the adhesive condition is good is 3.0. Figure 10 (A) shows the frequency spectrum of the sensor 20 attached to a rough concrete surface and elastic waves generated by the PLB method were detected. The index value was 1.6. Since this is below the reference value, the structure was polished according to the same procedure. Figure 10 (B) shows the frequency spectrum of the elastic waves generated by the PLB method again after polishing. The index value was 9.2, which exceeded the reference value. This system ensures a good adhesive condition. In this way, the adhesive condition determination device 25 determines the adhesive condition of the sensor 20 based on intensity information (amplitude statistics and energy) in different frequency bands of the elastic waves, rather than the peak frequency as in conventional methods. For example, the adhesive condition determination device 25 determines the adhesive condition of the sensor 20 based on the relationship between the intensity information in different frequency bands. Therefore, even if a slight deviation occurs due to the influence of noise or the like, it is possible to accurately determine the adhesion state of the sensor.

[0059] (Variation 1) In the above-described embodiment, the AD converter 250, the signal processing unit 251, the index value acquisition unit 252, the determination unit 253, the output control unit 254, and the output unit 255 are provided in a single housing. However, some of the functions of the AD converter 250, the signal processing unit 251, the index value acquisition unit 252, the determination unit 253, the output control unit 254, and the output unit 255 may be implemented in different devices. For example, the AD converter 250, the signal processing unit 251, the index value acquisition unit 252, and the determination unit 253 may be provided in a first device, and the output control unit 254 and the output unit 255 may be provided in a second device. In this case, the first device and the second device are connected to each other so as to be able to communicate with each other via a wire or wirelessly. The first device and the second device function as a single adhesive state determination device 25.

[0060] (Variation 2) In the above-described embodiment, the signal processing unit 251 converts the time-domain signal waveform into a frequency spectrum by fast Fourier transform processing of the signal waveform, and then acquires intensity information related to the signal intensity in different frequency bands. However, the signal processing unit 251 may be configured to acquire intensity information related to the signal intensity in different frequency bands using band-pass filters. In this configuration, the adhesive condition determination device 25 includes at least a first band-pass filter and a second band-pass filter. The first band-pass filter is a filter having a pass band in a low-frequency band. The second band-pass filter is a filter having a pass band in a high-frequency band. The signal processing unit 251 acquires intensity information in the low-frequency band based on the signal that has passed through the first band-pass filter, and acquires intensity information in the high-frequency band based on the signal that has passed through the second band-pass filter. The first band-pass filter and the second band-pass filter may be provided as analog circuits before inputting the signal to the AD converter 250, or may be provided as digital filters after the AD converter 250. When the first band-pass filter and the second band-pass filter are provided as digital filters after the AD converter 250 , they are provided between the AD converter 250 and the signal processing unit 251 .

[0061] With this configuration, it is not necessary to perform fast Fourier transform processing in the signal processing unit 251. Since fast Fourier transform processing is a process that imposes a high computational load, the processing load on the adhesive state determination device 25 can be reduced.

[0062] (Variation 3) In the above-described embodiment, the signal processing unit 251 is configured to convert a time-domain signal waveform into a frequency spectrum by fast Fourier transform processing of the signal waveform, but the signal processing unit 251 may acquire a frequency spectrum by a method other than fast Fourier transform processing. Methods other than fast Fourier transform processing include, for example, frequency conversion processing such as wavelet transform. When configured in this manner, the signal processing unit 251 has a time-frequency analysis unit that calculates the time-frequency characteristics of the time-series signal of the elastic wave, and acquires intensity information of the elastic wave in different frequency bands within a predetermined time range.

[0063] (Variation 4) The determination unit 253 may determine the adhesive state of the sensor 20 using a trained model that has been trained to receive strength information as input and output a determination result of the adhesive state of the sensor 20. When configured in this manner, the adhesive state determination device 25 does not need to include the index value acquisition unit 252. The determination unit 253 inputs the strength information acquired by the signal processing unit 251 into the trained model, and determines the determination result output from the trained model as the adhesive state of the sensor 20.

[0064] (Variation 5) In the above-described embodiment, an example was described in which there is one judgment threshold, but there may be multiple judgment thresholds. In this configuration, the judgment unit 253 may judge the adhesive state in at least three levels depending on the number of judgment thresholds that the index value exceeds among the multiple judgment thresholds. For example, when judging the adhesive state in three levels, the judgment unit 253 may express the adhesive state in order of best to worst, such as Adhesion State 1, Adhesion State 2, and Adhesion State 3. In this case, the index value is a numerical value indicating the quality of the adhesive state of the sensor, and for example, a larger numerical value indicates a better adhesive state and a smaller numerical value indicates a poorer adhesive state. In this case, an index value that exceeds a larger number of judgment thresholds can be evaluated as a better adhesive state, and an index value that exceeds a smaller number of judgment thresholds can be evaluated as a poor adhesive state.

[0065] (Variation 6) In the above-described embodiment, the low-frequency band and the high-frequency band are set on either side of the resonant frequency of the sensor 20. However, the low-frequency band and the high-frequency band may be set without sandwiching the resonant frequency of the sensor 20. Specifically, if the resonant frequency fr of the sensor 20 is sufficiently higher than the cutoff frequency of the high-pass filter in the adhesive condition determination device 25, the high-frequency band may be set slightly lower than the resonant frequency of the sensor 20. For example, if the resonant frequency fr of the sensor 20 is 300 kHz, the low-frequency components may be set to components around 20 kHz, which is near the cutoff frequency of the high-pass filter in the adhesive condition determination device 25, and the high-frequency components may be set to components around 100 kHz, which is lower than the resonant frequency of the sensor 20, 300 kHz. In other words, the low-frequency band is a range that includes 20 kHz, and the high-frequency band is a range that includes 100 kHz. While the ranges of the low-frequency band and the high-frequency band are similar to those described in the embodiment, the present embodiment differs in that the resonant frequency of the sensor 20 is not sandwiched between the low-frequency band and the high-frequency band. The high-pass filter in the adhesive state determination device 25 is provided in the signal processing unit 251.

[0066] As described above, a plurality of judgment thresholds may be provided. In such a configuration, the judgment unit 253 may judge the adhesive state in at least three levels depending on the number of judgment thresholds that the index value exceeds among the plurality of judgment thresholds. For example, when judging the adhesive state in three levels, the judgment unit 253 may express the adhesive state in order of best to worst, such as Adhesion State 1, Adhesion State 2, and Adhesion State 3.

[0067] (Variation 7) In the above-described embodiment, the configuration for acquiring intensity information in two frequency bands, i.e., a low-frequency band and a high-frequency band, as different frequency bands has been described. In contrast, the adhesion state determination device 25 may acquire intensity information in each of three or more frequency bands as different frequency bands and determine the adhesion state. When configured in this way, the signal processing unit 251 uses the frequency spectrum to divide the frequency spectrum into three or more frequency bands. For example, when dividing the frequency spectrum into three frequency bands, the signal processing unit 251 may use the frequency spectrum to divide it into a low-frequency band, a high-frequency band, and another frequency band. Note that the method of dividing into three or more levels may be any division as long as the frequency bands do not overlap. Then, the signal processing unit 251 acquires the amplitude values corresponding to the low-frequency band, the high-frequency band, and the other frequency bands respectively as intensity information. The amplitude value at this time can be a statistical value in each frequency band. The index value acquisition unit 252 draws an approximate straight line passing through the amplitude values corresponding to the low-frequency band, the high-frequency band, and the other frequency bands respectively acquired by the signal processing unit 251, and acquires the slope a of the approximate straight line as the index value.

[0068] An example will be given in the case where the slope in the direction where the amplitude increases as the frequency increases is positive, and the slope in the direction where the amplitude decreases as the frequency increases is negative. In this case, the determination unit 253 uses the determination threshold value th and determines that the adhesion state is good when the index value a has a smaller slope than the threshold value th (i.e., a < th). On the other hand, the determination unit 253 uses the determination threshold value th and determines that the adhesion state is poor when the index value a has a larger slope than the threshold value th (i.e., a > th). The threshold value th is, for example, 0.

[0069] Alternatively, the index value acquiring unit 252 may approximate the frequency spectrum itself, with the frequency range limited, with a straight line and acquire the slope of the line as the index value. For example, when the signal processing unit 251 acquires the frequency spectrum in the frequency range from 1 kHz to 1 MHz, the index value acquiring unit 252 can calculate the slope as the index value a by performing linear approximation within a limited range from 20 kHz, which is the cutoff frequency of the high-pass filter in the adhesive condition determining device 25, to 100 kHz, which is a sufficiently larger frequency.

[0070] As described above, a plurality of threshold values ​​th may be provided. In such a configuration, the determination unit 253 may determine the adhesive state in at least three levels depending on the number of threshold values ​​th that the index value exceeds among the plurality of threshold values ​​th. For example, when determining the adhesive state in three levels, the determination unit 253 may express the adhesive state in order of best to worst, such as adhesive state 1, adhesive state 2, and adhesive state 3.

[0071] According to at least one of the embodiments described above, the adhesive condition determination device 25 has a signal processing unit 251 that acquires intensity information in different frequency bands of elastic waves detected by one or more sensors 20 that detect elastic waves generated by an impact applied by an impact application unit 10 that applies an impact to a structure 50, and a determination unit 253 that determines the adhesive condition of the one or more sensors 20 based on the intensity information acquired by the signal processing unit 251, thereby making it possible to accurately determine the adhesive condition of the sensors.

[0072] Some of the processing performed by the adhesive condition determination device 25 in the above-described embodiment may be implemented by a computer. In this case, a program for implementing this function may be recorded on a computer-readable recording medium and loaded into a computer system for execution. The term "computer system" as used herein includes hardware such as an operating system (OS) and peripheral devices. The term "computer-readable recording medium" also refers to portable media such as flexible disks, optical magnetic disks, read-only memories (ROMs), and CD-ROMs, as well as storage devices such as hard disks built into computer systems. The term "computer-readable recording medium" may also include devices that dynamically store programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or telephone lines, or volatile memory within the computer systems that serve as servers or clients. The program may be designed to implement some of the aforementioned functions, or may be capable of implementing the aforementioned functions in combination with programs already stored in the computer system, or may be implemented using a programmable logic device such as an FPGA.

[0073] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0074] 10...impact applying unit, 20...sensor, 25...adhesion state determining device, 251...signal processing unit, 252...index value acquiring unit, 253...determining unit, 254...output control unit, 255...output unit

Claims

1. a signal processing unit that applies an impact to a structure and acquires intensity information regarding signal intensities in different frequency bands of elastic waves detected by one or more sensors that detect elastic waves generated by the impact applied by the impact application unit; and a determination unit that determines the adhesive state of the one or more sensors based on the strength information acquired by the signal processing unit; A sensor adhesion state determination device comprising:

2. an index value acquisition unit that acquires an index value for determining the adhesive state of the one or more sensors based on the strength information acquired by the signal processing unit, the determination unit determines the adhesion state of the one or more sensors based on the index value acquired by the index value acquisition unit. The sensor attachment state determination device according to claim 1 .

3. the signal processing unit calculates amplitude statistics in a first frequency band and a second frequency band that is a frequency band different from the first frequency band, the index value acquisition unit acquires, as the index value, a ratio or a difference between the statistical value of the amplitude in the first frequency band and the statistical value of the amplitude in the second frequency band. The sensor attachment state determination device according to claim 2 .

4. the signal processing unit calculates energy in a first frequency band and a second frequency band that is a frequency band different from the first frequency band; the index value acquisition unit acquires, as the index value, a ratio value or a difference value between the energy in the first frequency band and the energy in the second frequency band; The sensor attachment state determination device according to claim 2 .

5. the index value acquisition unit acquires, as the index value, a gradient of an approximation line passing through three or more different frequency bands based on the intensity information acquired by the signal processing unit; The sensor attachment state determination device according to claim 2 .

6. the determination unit determines that the adhesive state of the one or more sensors whose index value is less than a predetermined determination threshold is poor. The sensor adhesion state determination device according to claim 2 .

7. the determination unit determines the adhesion state of the one or more sensors in three or more stages based on a plurality of determination thresholds for the index value; The sensor adhesion state determination device according to claim 2 .

8. the determination unit inputs the strength information acquired by the signal processing unit into a trained model that has been trained to input the strength information and output a determination result of the adhesive state of the one or more sensors, and determines the determination result output from the trained model as the adhesive state of the one or more sensors. The sensor adhesion state determination device according to claim 1 .

9. the signal processing unit converts the time series signals of the elastic waves detected by the one or more sensors into a frequency domain to calculate a frequency spectrum, and acquires intensity information regarding signal intensities of the elastic waves in different frequency bands based on the calculated frequency spectrum; The sensor adhesion state determination device according to claim 1 .

10. The signal processing unit has a time-frequency analysis unit that calculates time-frequency characteristics of the time-series signal of the elastic wave detected by the one or more sensors, and acquires intensity information regarding signal intensity in different frequency bands of the elastic wave within a predetermined time range. The sensor adhesion state determination device according to claim 1 .

11. Further comprising a plurality of band pass filters with different pass bands, the signal processing unit acquires intensity information regarding signal intensities in different frequency bands of the elastic wave based on the signals that have passed through the plurality of band-pass filters. The sensor adhesion state determination device according to claim 1 .

12. the signal processing unit sets the lowest frequency of the different frequency bands to a frequency lower than a resonant frequency of the one or more sensors; The sensor adhesion state determination device according to claim 1 .

13. the signal processing unit sets the highest frequency among the different frequency bands to a frequency higher than a resonant frequency of the one or more sensors; The sensor adhesion state determination device according to claim 1 .

14. an impact applying unit that applies an impact to the structure; one or more sensors that detect elastic waves generated by the impact applied by the impact application unit; a signal processing unit that acquires intensity information regarding signal intensities in different frequency bands of the elastic waves detected by the one or more sensors; a determination unit that determines the adhesive state of the one or more sensors based on the strength information acquired by the signal processing unit; A sensor adhesion state determination system comprising:

15. applying an impact to a structure on which one or more sensors are installed; Detecting elastic waves generated by the applied impact, Obtaining intensity information regarding signal intensities of the detected elastic waves in different frequency bands; determining the adhesion state of the one or more sensors based on the acquired strength information; A method for determining the sensor adhesion state.

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