JP7270189B2 - Conversion factor calculation method - Google Patents

Description

The present invention relates to a conversion factor calculation method applied to an information board abnormality detection system for diagnosing an abnormality in the mounting state of an information board installed on a post.

As a method of detecting the mounting state of the information board installed on the pillar, the main method was to perform a periodic inspection by an inspector visually or using some kind of measuring instrument. Further, there is disclosed a conventional technique for simply and quickly quantitatively inspecting cracks that occur over time in an information board, which is an object of abnormality diagnosis of the mounting state (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 visible light is mixed in advance in an information board to be diagnosed. The information board is irradiated with a light source that emits ultraviolet light or blue visible light, and the generation of cracks is quantitatively determined by visual inspection or by analysis processing of images captured by a CCD camera or the like.

JP 2013-83493 A

However, the prior art has the following problems.
In Patent Document 1, although it enables quantitative abnormality diagnosis of the mounting state, it is based on periodic inspections by inspectors. Furthermore, in Patent Document 1, it is necessary to mix a fluorescent dye in advance in the information board to be diagnosed as an abnormality.

On the other hand, in recent years, there has been a demand for an abnormality diagnosis system capable of diagnosing the mounting state of the information plate in a shorter cycle than the periodical inspection and without the intervention of an inspector. In addition, there is an increasing need to quantitatively diagnose the deterioration of the installation state of the information board installed on the post over a long period of time. Furthermore, it is desired that the existing information boards as well as the new information boards can be easily handled.

Accurately measure and count the repeated stress applied to the posts on which the information boards are installed to detect the accumulated fatigue of the information boards. Prediction of cumulative fatigue can be made.

A strain gauge or the like is used to measure the stress applied to the strut. However, strain gauges are inherently poor in durability and have the drawback of being unsuitable for long-term measurements.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a conversion coefficient calculation method capable of calculating a conversion coefficient that associates acceleration information and strain information with high accuracy. .

The conversion coefficient calculation method according to the present invention includes an acceleration sensor installed on a support or an information board attached to the support and measuring acceleration information, and a stress applied to the support based on the acceleration information measured by the acceleration sensor. Abnormality detection of an information board provided with a diagnostic unit that detects accumulated fatigue of the information board by estimating, and a strain measurement sensor that temporarily measures strain information at or near a stress-concentrated portion of the strut In a system, a conversion coefficient calculation method for estimating a conversion coefficient for converting acceleration information to strain information by a diagnosis unit, comprising acceleration information measured by an acceleration sensor and strain information measured by a strain measurement sensor are obtained at the same time for a predetermined period to obtain the time-series data of the acceleration information and the time-series data of the strain information, and for both the time-series data of the acceleration information and the time-series data of the strain information, a second step of generating time-series data of acceleration information after filtering and time-series data of strain information after filtering by performing narrowband bandpass filtering with the same characteristics including the primary resonance frequency; A third step of estimating a conversion coefficient by calculating a regression line between the acceleration information and the strain information based on the filtered time-series data of the acceleration information and the filtered time-series data of the strain information. and

According to the present invention, there is provided a method of removing the effects of time lag and phase difference and appropriately calculating conversion coefficients according to the installation environment. As a result, it is possible to obtain a conversion coefficient calculation method capable of calculating a conversion coefficient that associates acceleration information and strain information with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS In Embodiment 1 of this invention, it is explanatory drawing which showed the structure which is abnormality diagnosis object, (A) is a front view, (B) is an enlarged view of the base part of a support|pillar. 1 is a configuration diagram of an information board abnormality detection system according to Embodiment 1 of the present invention; FIG. 4 is a flow chart showing a series of processes for measuring the correlation between strain and acceleration executed in the information board abnormality detection system according to Embodiment 1 of the present invention. 4 is a diagram showing an example of correlation between acceleration information and strain information in Embodiment 1 of the present invention; FIG. 4 is a flow chart showing a series of abnormality detection processes executed in the information board abnormality detection system according to Embodiment 1 of the present invention. 10 is a flow chart showing a series of processes of a first technique for obtaining conversion coefficients based on results of simultaneous measurement of acceleration and strain in Embodiment 2 of the present invention. FIG. 10 is an explanatory diagram showing each stage of processing in the first method according to the second embodiment of the present invention, divided into (A) to (G). 10 is a flow chart showing a series of processes of a second method of obtaining conversion coefficients based on results of simultaneous measurement of acceleration and strain in Embodiment 2 of the present invention. FIG. 10 is an explanatory diagram showing each stage of processing in the second method according to the second embodiment of the present invention, divided into (A) to (E).

A preferred embodiment of the conversion coefficient calculation method for information boards according to the present invention will be described below with reference to the drawings.
The present invention relates to a conversion factor calculation method applied to a system for diagnosing abnormalities in the mounting state of an information plate by obtaining the accumulated fatigue of the information plate based on the stress applied to the post. In the information board fatigue prediction system using such an acceleration sensor, the performance depends on how accurately the acceleration-stress conversion coefficient can be calculated. A technical feature of the present invention is that the relationship between acceleration information and strain (stress) information is calculated after error factors have been removed, so conversion coefficients can be calculated with extremely high accuracy.

Accurately analyze the correlation between the measured value of the temporarily installed strain gauge and the measured value of the acceleration information in advance. From the previously analyzed correlations, the stresses applied to the struts can be estimated with high accuracy to determine the accumulated fatigue.

Embodiment 1.
1A and 1B are explanatory diagrams showing a structure to be subjected to abnormality diagnosis according to Embodiment 1 of the present invention, where (A) is a front view and (B) is an enlarged view of the base portion of a column. In FIG. 1, the structure whose installation state is to be diagnosed is constructed by attaching an information board 1 to the upper portion of a support 2 . The information board 1 is, for example, a road information board installed on the road, and the pedestrian or the driver can acquire necessary information by visually recognizing the road information board.

A structure in which the information board 1 is installed on the top of the pillar 2 has a top load. Therefore, the strut 2 may be fatigued and damaged due to repeated stress vibrations caused by an external force. In order to prevent fatal damage to the column 2, it is important to observe the accumulated fatigue of the column 2 and know the life of the column 2.

As noted above, strain gauges may be used to measure the stress applied to the struts. However, strain gauges are inherently less durable and are not suitable for long-term measurements. Considering durability, it is conceivable to substitute an acceleration sensor.

Here, in order to accurately measure accumulated fatigue from acceleration, it is necessary to accurately calculate the stress applied to the stress concentrated portion of the strut from the acceleration information measured by the acceleration sensor. However, even with the same structure including the information board 1 and the struts 2, the relationship between acceleration and stress differs due to individual differences and slight differences in design.

Therefore, in the present invention, by adopting the configuration described below, an abnormality detection system for an information board is realized that improves both the accuracy of measurement of accumulated fatigue and the durability of the sensor.
(Configuration 1) A configuration is provided in which accumulated fatigue is estimated using the acceleration sensor 20 during normal operation. This configuration is intended to improve the durability of the sensor.

(Configuration 2) In the preliminary preparation stage before actual operation, the strain measurement sensor 40 can be installed on or near the stress concentration portion of the column, which is the target of accumulated fatigue measurement, and the acceleration sensor 20 and the strain measurement sensor 40 By performing simultaneous measurement with , it has a configuration that accurately obtains the correlation between the two. With this configuration, it is possible to accurately calculate the stress applied to the stress concentrated portion of the column from the measurement result of the acceleration sensor 20 without using the strain measurement sensor 40 during actual operation.

As shown in FIG. 1, in the information board abnormality detection system according to the first embodiment, an acceleration sensor 20 is installed on the top of the support column 2, and acceleration information measured by the acceleration sensor 20 is processed by a relay device 30. Configuration 1 is realized by doing so.

Further, as shown in FIG. 1, in the information board abnormality detection system according to the first embodiment, a strain measuring sensor 40 is temporarily placed in the stress concentration portion of the support 2 or in the vicinity thereof in a preliminary preparation stage before actual operation. to be installed. Here, the strain measurement sensor 40 can be installed in the stress concentrated portion of the support 2 or in the vicinity thereof so as to be held by the frictional force of compression. Then, in the preliminary preparation stage, at the same timing, the acceleration information measured by the acceleration sensor 20 and the strain information of the stress concentration portion measured by the strain measurement sensor 40 are read into the relay device 30 for a predetermined period of time. Configuration 2 is realized by accurately obtaining the correlation.

Next, the configuration of the information board abnormality detection system according to the first embodiment will be described with reference to FIG. FIG. 2 is a configuration diagram of an information board abnormality detection system according to Embodiment 1 of the present invention. The information board abnormality detection system according to the first embodiment includes a data processing device 10, an acceleration sensor 20, a relay device 30, and N (N is an integer) strain measurement sensors 40(1) to 40(N). configured with.

In the information board abnormality detection system according to Embodiment 1, if at least one strain measurement sensor 40 is temporarily installed, it is possible to obtain the correlation between the acceleration information and the strain information. Further, the functions of the N strain measurement sensors when a plurality of sensors are used are all common. Therefore, in the following description, when there is no need to distinguish between the strain measuring sensors, the strain measuring sensors are simply referred to as the strain measuring sensors 40 without using the subscripts (1) to (N).

The acceleration sensor 20 has a sensor section 21 and an acceleration information output section 22 and is installed on the top of the column 2 . It should be noted that the installation position of the acceleration sensor 20 is not limited to the top of the column 2, and can be installed in a position other than the top of the column 2, or on the information board 1 attached to the column 2.

Generally, the acceleration sensor 20 has a longer life than the strain measurement sensor 40, and abnormality determination during actual operation is performed using only the acceleration information measured by the acceleration sensor 20.

The sensor unit 21 is, for example, a three-axis acceleration sensor that uses a thin-film crystal oscillator. In addition, the acceleration information output unit 22 converts the analog signal related to the three-axis acceleration at the top of the column 2 into a digital signal at a predetermined sampling rate (for example, a sampling rate of 50 Hz), and sends it to the relay device 30 as acceleration information. Send.

On the other hand, the strain measuring sensor 40 is installed so as to be temporarily held at or near the stress concentration portion of the column 2 before the operation of the abnormality detection device is started. The stress concentration portion is a general term for a position including a portion where stress is concentrated or its vicinity. Furthermore, the strain measurement sensor 40 can be connected to the relay device 30 .

The sensor unit 41 enables stress measurement using, for example, a friction strain gauge. Also, the distortion information output unit 42 converts the analog signal detected by the sensor unit 41 into a digital signal at a predetermined sampling rate (for example, a sampling rate of 50 Hz), and transmits the digital signal as distortion information to the relay device 30 .

The relay device 30 is attached to the pillar 2 as shown in FIG. Based on the acceleration information received from the acceleration information output unit 22 in the acceleration sensor 20 and the strain information received from the strain information output unit 42 in the strain measurement sensor 40, the relay device 30 outputs the acceleration information and the strain information. to find the correlation.

More specifically, the relay device 30 transmits the acceleration information measured by the acceleration sensor 20 and the strain information measured by the strain measurement sensor 40 at the same time while the strain measurement sensor 40 is temporarily installed. to calculate the correlation between the acceleration information and the strain information. The correlation here means a conversion formula or a conversion coefficient for converting acceleration information into strain information.

In addition, as shown in FIG. 1, when a plurality of strain measuring sensors 40 are installed in the stress concentrated portion, it is possible to obtain an appropriate correlation according to each installation location.

By using the correlation calculated in advance, the relay device 30 can convert the acceleration information measured by the acceleration sensor into strain information after the strain measurement sensor 40 is removed. Then, the relay device 30 sequentially stores the distortion information generated by the conversion in the storage unit together with the time information when the acceleration information is acquired. Moreover, the relay device 30 transmits the distortion information and the time information to the data processing device 10 each time the distortion information is generated.

Based on the strain information and time information received from the relay device 30, the diagnostic unit 11 in the data processing device 10 estimates the accumulated fatigue of the support column 2 to be diagnosed, and determines whether it is normal or abnormal. . Moreover, the data processing apparatus 10 can store the strain information and the measurement time information in a large-capacity storage unit when the strain information and the measurement time information need to be stored for a long period of time.

In this way, the relay device 30 simultaneously measures the acceleration information and the strain information for a certain period of time before starting the operation of the abnormality detection device, and analyzes the correspondence relationship between the two, thereby converting acceleration into stress. It is possible to calculate a conversion formula or a conversion coefficient for , as an appropriate value according to the installation environment. As a result, it is possible to realize an information board abnormality detection system capable of absorbing the relationship between acceleration and stress due to individual differences in structures and exhibiting stable diagnostic performance.

A series of abnormality diagnosis techniques based on such accumulated fatigue will be described below with reference to FIGS. 3 to 5. FIG. FIG. 3 is a flowchart showing a series of processes for measuring the correlation between strain and acceleration executed in the information board abnormality detection system according to the first embodiment of the present invention. Moreover, FIG. 4 is a diagram showing an example of the correlation between acceleration information and strain information in Embodiment 1 of the present invention. FIG. 5 is a flow chart showing a series of abnormality detection processes executed in the information board abnormality detection system according to the first embodiment of the present invention.

Note that steps S301 to S308 shown in FIG. 3 are all steps performed in the preparatory stage before actual operation, and include both processing by the operator and processing performed by the anomaly detection system. .

In step S301, the operator installs the strain measuring sensor 40 at or near the stress concentration portion. Furthermore, in step S302, the operator connects the strain measurement sensor to the relay device 30. FIG. As a result, in step S303, a connection is established, and the relay device 30 can read strain information output from the temporarily installed strain measurement sensor 40. FIG.

Next, in step S304, the relay device 30 simultaneously measures the acceleration information and the strain information, and sequentially stores the measurement results in the storage unit. It should be noted that the operator may intentionally vibrate the column 2 during the simultaneous measurement in step S304. Then, in step S305, the relay device 30 terminates the simultaneous measurement after a predetermined period of time has elapsed.

Next, in step S306, the relay device 30 analyzes the measurement result in step S304 to calculate a conversion coefficient indicating the correlation between the acceleration information and the strain information. When the measurement results obtained in step S304 are plotted on a plane with acceleration on the horizontal axis and strain on the vertical axis, the relationship shown in FIG. 4 is obtained as an example. Therefore, the relay device 30 can calculate a conversion coefficient for converting acceleration into strain from the measurement result obtained in step S304.

Next, in step S<b>307 , the operator disconnects the temporarily connected relay device 30 and strain measurement sensor 40 . Further, in step S308, the operator removes the strain measurement sensor 40, which has been temporarily installed to calculate the conversion coefficient, from the column 2, and ends the series of processes.

Note that in the above description of the flowchart of FIG. 3, the relay device 30 that has acquired the distortion information is configured to calculate the conversion coefficient. However, the present invention is not limited to such configurations. An arithmetic processing unit is provided on the side of the temporarily installed strain measurement sensor 40, and acceleration information is acquired via the relay device 30, so that the conversion coefficient is calculated in the arithmetic processing unit, and the calculated conversion coefficient is sent to the relay device 30. It is also possible to have a configuration for sending back.

Further, the relay device 30 can also perform the following filtering process when calculating the conversion formula or the conversion coefficient. That is, the relay device 30 performs filtering processing for removing unnecessary high-frequency components and low-frequency components that cause errors from the acquired acceleration information and strain information, and then uses the calculated result of the correlation between the two. can be used to estimate transform equations or transform coefficients. By performing such filtering processing, it becomes possible to calculate the correlation with higher accuracy.

Next, the flow of a series of abnormality detection processing will be described using the flowchart of FIG. In step S<b>501 , the relay device 30 acquires acceleration information from the acceleration sensor 20 . Next, in step S502, the relay device 30 converts the acceleration information acquired in step S501 into strain using a conversion coefficient obtained in advance.

Then, in step S503, the relay device 30 performs abnormality diagnosis of the information board by calculating the accumulated fatigue of the structure including the information board 1 and the pillars 2 based on the converted strain.

As described above, according to Embodiment 1, a strain measuring sensor that is not suitable for long-term measurement is temporarily installed, a conversion coefficient for converting acceleration to strain is calculated, and during actual operation, It has a configuration that can convert acceleration information acquired from an acceleration sensor into strain using a conversion coefficient calculated in advance. As a result, the accumulated fatigue of the structure to be diagnosed can be accurately calculated using an acceleration sensor suitable for long-term measurement. In other words, an information board abnormality diagnosis system capable of quantitatively diagnosing the accumulated fatigue of the information board over a long period of time based on the stress applied to the strut without requiring regular inspections by an inspector. realizable.

Embodiment 2.
In the second embodiment, two specific methods for obtaining conversion coefficients based on the results of simultaneous measurement of acceleration and strain will be described. As preprocessing common to each method, only the components near the resonance frequency of the object to be measured are extracted by a digital filter.

<First method>
The first method will be described with reference to FIGS. 6 and 7. FIG. FIG. 6 is a flow chart showing a series of processes of the first method for obtaining conversion coefficients based on the results of simultaneous measurement of acceleration and strain in Embodiment 2 of the present invention. FIG. 7 is an explanatory diagram showing each stage of processing in the first method according to Embodiment 2 of the present invention, and is divided into (A) to (G).

In step S601, the relay device 30 simultaneously measures acceleration and strain for a predetermined period of time. This processing corresponds to the processing of steps S304 and S305 in FIG. An example of raw waveforms acquired at the same time is shown in FIG. 7(A).

Next, in step S602, the relay device 30 acquires resonance frequencies related to acceleration and strain by executing FFT processing. An example of the waveform from which the resonance frequency was obtained is shown in FIG. 7(B).

Next, in step S603, if the resonance frequencies obtained in step S602 are clearly different, the relay device 30 determines that the measurement has failed, and repeats the processing of steps S601 and S602.

Next, in step S604, the relay device 30 performs band-pass filter processing using a digital filter on each of the acceleration time-series data and the strain time-series data, and extracts only components around the resonance frequency. Here, the band-pass filter processing applied to each of the time-series data of acceleration and the time-series data of strain corresponds to narrow-band band-pass filter processing with the same characteristics including the primary resonance frequency of the strut 2 . An example of a waveform obtained by extracting only the components around the resonance frequency is shown in FIG. 7(C).

Next, in step S605, the relay device 30 shifts the time-series data of the strain after the band-pass filter processing to the time-series data of the acceleration after the band-pass filter processing in the time direction. The cross-correlation coefficient is calculated by shifting the sampling by one within the range of the period. An example of the state of shifting in the time direction is shown in FIG. 7(D), and an example of the state with the highest cross-correlation coefficient is shown in FIG. 7(E).

Next, in step S606, relay device 30 obtains a cross-correlation function by plotting cross-correlation coefficients over the entire range shifted by one sampling within a predetermined range. An example of the obtained cross-correlation function is shown in FIG. 7(F).

Next, in step S607, the relay device 30 acquires the time lag at which the cross-correlation function shown in FIG. 7F takes the maximum value.

Next, in step S608, the relay device 30 plots the acceleration data (X) and strain data (Y) at the same time on the XY plane, taking into consideration the time lag relationship obtained in step S607. , to create a scatterplot. An example of the generated scatter plot is shown in FIG. 7(G).

Next, in step S609, the relay device 30 removes peculiar outliers from the data forming the scatter diagram.

Next, in step S610, the relay device 30 calculates a regression line based on the scatter diagram and acquires the slope value of the regression line.

Finally, in step S611, the relay device 30 multiplies the obtained tilt value by a conversion parameter corresponding to the material-dependent strain to obtain a conversion coefficient, and ends the series of processes. A transform coefficient can be obtained by applying such a first method.

That is, the relay device 30 measures the cross-correlation by sliding the time-series data of the distortion information after filtering in the time direction with respect to the time-series data of the acceleration information after filtering through the series of processes described above, The conversion coefficient can be estimated by calculating the regression line between the acceleration information and the strain information under the condition that the time relationship is slid to the highest correlation.

<Second method>
The second method will be described with reference to FIGS. 8 and 9. FIG. FIG. 8 is a flow chart showing a series of processes of the second method of obtaining conversion coefficients based on the results of simultaneous measurement of acceleration and strain in Embodiment 2 of the present invention. FIG. 9 is an explanatory diagram showing each stage of processing in the second method according to Embodiment 2 of the present invention, and is divided into (A) to (E).

In step S801, the relay device 30 simultaneously measures acceleration and strain for a predetermined period of time. This processing corresponds to the processing of steps S304 and S305 in FIG. An example of raw waveforms acquired at the same time is shown in FIG. 9(A).

Next, in step S802, the relay device 30 acquires resonance frequencies related to acceleration and strain by executing FFT processing. An example of the waveform from which the resonance frequency was obtained is shown in FIG. 9(B).

Next, in step S803, if the resonance frequencies obtained in step S802 are clearly different, the relay device 30 determines that the measurement has failed, and repeats the processing of steps S801 and S802.

Next, in step S804, relay device 30 performs band-pass filter processing using a digital filter on each of the time-series data of acceleration and the time-series data of strain, and extracts only components around the resonance frequency. Here, the band-pass filter processing applied to each of the time-series data of acceleration and the time-series data of strain corresponds to narrow-band band-pass filter processing with the same characteristics including the primary resonance frequency of the strut 2 . An example of a waveform obtained by extracting only the components around the resonance frequency is shown in FIG. 9(C).

Note that the processing of steps S801 to S804 is the same as steps S601 to S604 in the first method.

Next, in step S805, the relay device 30 performs cycle count processing of the acceleration data by the rainflow method to acquire total amplitude data. Similarly, in step S806, the relay device 30 performs cycle count processing of distortion data by the rainflow method, and obtains total amplitude data. The results of performing cycle count processing on the acceleration and strain data are shown in FIG. 9(D). Although the rainflow method is used for the cycle count processing here, the cycle count processing method is not limited to this.

Next, in step S807, the relay device 30 sorts the acceleration total amplitude data in descending order. Similarly, in step S808, relay device 30 sorts the distortion total amplitude data in descending order.

Next, in step S809, the relay device 30 plots the sorted acceleration total amplitude data (X) and the sorted strain total amplitude data (Y) on the XY plane to create a scatter diagram. An example of the generated scatter plot is shown in FIG. 9(E).

Next, in step S810, the relay device 30 removes the strain total amplitude data having a predetermined value or less and the acceleration total amplitude data corresponding to the strain total amplitude data from the data forming the scatter diagram. remove outliers.

Next, in step S811, the relay device 30 calculates a regression line based on the scatter diagram and acquires the slope value of the regression line.

Finally, in step S812, the relay device 30 multiplies the obtained tilt value by a conversion parameter corresponding to the material-dependent strain to obtain a conversion coefficient, and ends the series of processes. A transform coefficient can be obtained by applying such a second method.

That is, relay device 30 performs cycle count processing on each of the time-series data of the acceleration information after filtering and the time-series data of strain information after filtering through the series of processes described above. Strain full amplitude data is generated, data within a preset range is extracted from the strain full amplitude data as strain full amplitude extraction data, and strain full amplitude extraction data and acceleration full amplitude corresponding to the strain full amplitude extraction data are obtained. A conversion coefficient can be estimated by calculating a regression line with the amplitude data.

The processing of steps S811 and S812 is the same as that of steps S610 and S611 in the first method.

As described above, according to the second embodiment, the regression line between the acceleration data and the strain data is obtained, and the conversion coefficient for converting the acceleration data into the strain data is obtained from the slope of the regression line. It has Acceleration data and strain data are affected by time lag due to overhead such as communication processing, and a phase difference occurs between the measured waveforms of both. Therefore, it is difficult to calculate conversion coefficients simply by measuring waveforms of acceleration data and strain data.

Therefore, in the second embodiment, by applying the first method and the second method as described above, it is possible to remove the effects of the time lag and the phase difference and to calculate the transform coefficients with high accuracy. and As a result, the accumulated fatigue of the structure to be diagnosed can be accurately calculated using an acceleration sensor suitable for long-term measurement. In other words, an information board abnormality diagnosis system capable of quantitatively diagnosing the accumulated fatigue of the information board over a long period of time based on the stress applied to the strut without requiring regular inspections by an inspector. realizable.

1 information board, 2 support, 10 data processing device, 11 diagnosis unit, 20 acceleration sensor, 21 sensor unit, 22 acceleration information output unit, 30 relay device, 40 strain measurement sensor, 41 sensor unit, 42 strain information output unit.

Claims (4)

an acceleration sensor installed on a support or an information board attached to the support to measure acceleration information;
a diagnosis unit that detects accumulated fatigue of the information plate by estimating the stress applied to the support from the acceleration information measured by the acceleration sensor;
a strain measuring sensor that temporarily measures strain information at or near a stress-concentrated portion of the support;
A conversion coefficient calculation method for estimating a conversion coefficient for converting from the acceleration information to the strain information by the diagnosis unit,
The acceleration information measured by the acceleration sensor and the strain information measured by the strain measuring sensor are acquired at the same time for a predetermined period of time to acquire time-series data of the acceleration information and time-series data of the strain information. a first step to
By subjecting both the time-series data of the acceleration information and the time-series data of the strain information to narrowband band-pass filter processing having the same characteristics including the primary resonance frequency of the strut, the acceleration information after filtering is filtered. a second step of generating time series data of and filtered strain information time series data;
The conversion coefficient is estimated by calculating a regression line between the acceleration information and the strain information based on the time-series data of the acceleration information after the filtering and the time-series data of the strain information after the filtering. a third step;
A conversion factor calculation method having The third step measures cross-correlation by sliding the filtered time-series data of the strain information in the time direction with respect to the time-series data of the filtered acceleration information, and measures the cross-correlation at the time of the highest correlation. 2. The conversion coefficient calculation method according to claim 1, further comprising a correlation processing step of estimating the conversion coefficient by calculating a regression line between acceleration information and strain information under a condition of sliding to a relation. The third step is
a generation step of generating acceleration total amplitude data and strain total amplitude data by performing cycle count processing on each of the filtered acceleration information time series data and the filtered strain information time series data;
Data within a preset range is extracted from the strain full amplitude data as strain full amplitude extraction data, and regression is performed on the strain full amplitude extraction data and the acceleration full amplitude data corresponding to the strain full amplitude extraction data. an estimating step of estimating the transform coefficients by calculating a straight line;
The transform coefficient calculation method according to claim 1, comprising: 4. The conversion factor calculation method according to any one of claims 1 to 3, wherein the strain measurement sensor can be installed so as to be held by frictional force due to compression.

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