CN113029242B - Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system - Google Patents
Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system Download PDFInfo
- Publication number
- CN113029242B CN113029242B CN202110272056.2A CN202110272056A CN113029242B CN 113029242 B CN113029242 B CN 113029242B CN 202110272056 A CN202110272056 A CN 202110272056A CN 113029242 B CN113029242 B CN 113029242B
- Authority
- CN
- China
- Prior art keywords
- sensor
- data
- characteristic data
- determining
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012544 monitoring process Methods 0.000 title claims abstract description 35
- 239000000835 fiber Substances 0.000 title claims abstract description 32
- 230000036541 health Effects 0.000 title claims abstract description 23
- 238000003745 diagnosis Methods 0.000 title abstract description 26
- 230000005856 abnormality Effects 0.000 claims abstract description 47
- 230000002159 abnormal effect Effects 0.000 claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims description 25
- 230000002776 aggregation Effects 0.000 claims description 22
- 239000002131 composite material Substances 0.000 claims description 10
- 238000005070 sampling Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000001914 filtration Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004220 aggregation Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 230000035882 stress Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012567 pattern recognition method Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D5/00—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable
- G01D5/26—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light
- G01D5/268—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light using optical fibres
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D5/00—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable
- G01D5/26—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light
- G01D5/32—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light
- G01D5/34—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells
- G01D5/353—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre
Abstract
The invention discloses a method for diagnosing abnormity of a fiber bragg grating sensor in a structural health monitoring system, which comprises the following steps: acquiring original output signals of a plurality of sensors arranged in a monitoring structure in the same time period; extracting feature data reflecting an operation state of each sensor based on a raw output signal of each sensor; determining a comprehensive abnormality index of each sensor based on the characteristic data of each sensor; an abnormal state of each sensor is diagnosed based on a known criterion and the integrated abnormality index of each sensor. By applying the method and the device, the accuracy of the sensor abnormity diagnosis can be realized in a simple, effective and easily realized mode.
Description
Technical Field
The invention belongs to the technical field of measurement, particularly relates to a sensor technology, and more particularly relates to a fiber bragg grating sensor abnormity diagnosis method in a structural health monitoring system.
Background
A Fiber Grating Sensor (Fiber Grating Sensor) belongs to one type of Fiber sensors, and the sensing process based on the Fiber Grating obtains sensing information by modulating the wavelength of Fiber Bragg through external physical parameters, and is a wavelength modulation type Fiber Sensor. In a large-scale space structure health monitoring system, a fiber grating strain sensor is often used for monitoring the structure stress, so as to realize real-time evaluation on the stress state of the structure. The method has a great number of application examples in structures such as cable net structures (such as large stadiums), large radio telescopes (such as FAST spherical radio telescopes with 500-meter calibers), and the like. In practical application, the number of fiber grating sensors arranged in the structural health monitoring system is large, the working environment is complex, and faults are easy to occur due to external interference, external force damage, natural aging, mounting failure, line faults and the like, so that the measured point data of the structural health monitoring system is unreliable, and the performance of the structural health monitoring system is further influenced. Therefore, in the structural health monitoring system, it is necessary to diagnose the abnormal state of the sensor and timely maintain the fault point.
In the prior art, the conventional fiber grating sensor abnormity diagnosis is mostly realized according to the judgment of a sensor output signal threshold, and a measuring point with a larger output deviation from an estimated value is removed or repaired by interpolation. Or through a manual observation mode, the historical output data tracks of the sensors are observed one by one, whether the states of the sensors are good or not is judged, and the states of the sensors are further checked in a manual field inspection mode. The method can also be based on a pattern recognition method, system modeling is carried out, various anomalies are simulated, anomaly data are obtained, an algorithm is trained, and the pattern recognition method suitable for anomaly diagnosis is obtained.
The fiber bragg grating sensors arranged in the structural health monitoring system, especially in a large-scale space structure, are usually more in number, often reach hundreds, the sampling data volume is large, when the sampling period is 1s, the data of a single measuring point can reach more than 250 ten thousand per month, the data volume is large, and the analysis is difficult. And during working, the acquisition system may be occasionally closed according to the working condition requirements of the object to be measured, resulting in discontinuity of data. Meanwhile, when the interference from the outside is large, outliers may occur. Therefore, a single threshold jump discrimination is not sufficient to accurately discriminate whether the operating state of the sensor is normal. On the other hand, although the manual observation has a more accurate judgment result, the workload is too large for a large number of measuring points, and the measurement is difficult to be completed timely and effectively. The method based on pattern recognition needs a large amount of simulation experiments, training samples and priori knowledge, and is difficult to realize.
Disclosure of Invention
The invention aims to provide an abnormity diagnosis method of a fiber bragg grating sensor in a structural health monitoring system, which is used for extracting and determining a comprehensive abnormity index of the sensor based on the characteristics of output signals of the sensor, identifying the abnormal state of the sensor by using the comprehensive abnormity index and realizing the abnormity diagnosis accuracy of the sensor in a simple, effective and easily-realized mode.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme:
a fiber bragg grating sensor abnormity diagnosis method in a structural health monitoring system comprises the following steps:
acquiring original output signals of a plurality of sensors arranged in a monitoring structure in the same time period;
extracting feature data reflecting an operation state of each sensor based on a raw output signal of each sensor;
determining a composite anomaly index for each sensor based on the characteristic data for each sensor;
an abnormal state of each sensor is diagnosed based on a known criterion and the integrated abnormality index of each sensor.
In one preferred embodiment, the characteristic data reflecting the operating state of the sensor includes at least time-domain energy value data reflecting the intensity of fluctuation in the signal output from the sensor and frequency-domain principal component period data reflecting the period of action of the principal load at the measuring point where the sensor is located.
In one preferred embodiment, extracting the time-domain energy value data of each sensor based on a raw output signal of the sensor specifically includes:
performing compression processing on the original output signal of the sensor to obtain compressed data with reduced data volume;
calculating an average of the compressed data
Calculating a data value x' (n) of the compressed data dequantization:
n＝1,2,…,l 1 ，l 1 is the length, x, of the compressed data * (n) is a data value in the compressed data;
calculating time domain energy value data E according to a time domain energy value calculation formula:
in one preferred embodiment, extracting the frequency domain principal component period data of each sensor based on the raw output signal of the sensor specifically includes:
performing compression processing on the original output signal of the sensor to obtain compressed data with reduced data volume;
performing a fourier transform on the compressed data to obtain a spectral function of the signal:
in the formula
l 2 Is the length of the compressed data, x (a) is the data value in the compressed data;
traversing the spectrum function, searching a maximum value max (X (k)), determining the maximum value as a frequency principal component, and acquiring a frequency k corresponding to the maximum value max ；
And converting the period T corresponding to the frequency principal component according to the following formula, and determining the period T as the frequency domain principal component period data:
in one preferred embodiment, the characteristic data reflecting the operation state of the sensor further includes one or more of signal length, signal standard deviation, signal amplitude, signal variance, and signal peak index.
In one preferred embodiment, the compressing the original output signal of the sensor to obtain compressed data with reduced data size specifically includes:
extracting wavelength value data in the original output signal;
acquiring the wavelength value data according to a set sampling period to obtain sampled data;
and carrying out low-pass filtering on the sampled data by adopting a low-pass filter to obtain the compressed data.
In one preferred embodiment, the determining the comprehensive abnormality index of each sensor based on the characteristic data of each sensor specifically includes:
taking all the characteristic data of each sensor as a row of a matrix, and taking the same type of characteristic data of all the sensors as columns of the matrix to form a characteristic matrix;
determining a one-dimensional feature aggregation center Y for each column in the feature matrix i1 (center); i1 ═ 1,2, …, p; p is the number of the characteristic data;
calculating a feature standard deviation for each column in the feature matrix
m is the total number of all sensors, i2 is 1,2, …, m, Y i1 (i2) For the signature data value at column i1 at row i2,
is the average of all the characteristic data values in column i1, Y i1 The ith 1 feature data;
determining a composite anomaly index d (i2) for each sensor:
in one preferred embodiment, the one-dimensional feature aggregation center Y of each column in the feature matrix is determined by searching a point closest to the aggregation center as an aggregation center point through loop iteration i1 (center)。
In one preferred embodiment, the diagnosing the abnormal state of each sensor according to the known criterion and the comprehensive abnormality index of each sensor specifically includes:
determining an abnormality index mean from the composite abnormality index d (i2) for all sensors
If the comprehensive abnormality index d (i2) of the i2 th sensor satisfies:
determining that the i2 th sensor is in a fault state;
if the comprehensive abnormality index d (i2) of the i2 th sensor satisfies:
determining that the i2 th sensor is in an abnormal state;
if the composite abnormality index d (i2) of the i2 th sensor satisfies:
judging that the i2 th sensor is in a normal state;
wherein, the coefficient k is determined 1 、k 2 Are all positive numbers greater than 1, and k 1 ＞k 2 。
In one preferred embodiment, the method further comprises:
acquiring an average value of target characteristic data of a plurality of sensors and the target characteristic data value of the sensor in the fault state, and comparing the target characteristic data value of the sensor in the fault state with the average value of the target characteristic data to obtain a comparison result;
and determining the fault type of the sensor in the fault state according to the comparison result and the corresponding relation between the known fault type and the comparison result.
Compared with the prior art, the invention has the advantages and positive effects that: the invention provides a fiber grating sensor abnormity diagnosis method in a structural health monitoring system, which extracts characteristic data reflecting the running state of a sensor from an original output signal of the sensor, determines a comprehensive abnormity index of the sensor according to the characteristic data, and diagnoses the abnormity state of the sensor according to the comprehensive abnormity index; according to the diagnosis method, the abnormal diagnosis of the sensor can be automatically performed based on the characteristics of the signal data output by the sensor, training samples and priori knowledge are not needed, the automatic abnormal diagnosis is completed in a simple and easily-realized mode, the problem of large workload of manual inspection is solved, and the working efficiency of abnormal diagnosis of the sensor is improved.
Other features and advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a method for diagnosing anomalies in a fiber grating sensor in a structural health monitoring system in accordance with the present invention;
FIG. 2 is a waveform diagram of an original output signal in another embodiment of the fiber grating sensor abnormality diagnosis method of the present invention;
FIG. 3 is a waveform of the raw output signal of FIG. 2 after sampling the data;
FIG. 4 is a waveform of the compressed data after filtering of the sampled data waveform of FIG. 3;
FIG. 5 is a frequency domain plot of the waveform of the compressed data of FIG. 4;
fig. 6 is a schematic diagram of a comprehensive abnormality index of a fiber grating sensor according to a specific example of the abnormality diagnosis method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and examples.
Firstly, the technical idea of the invention is briefly described:
in the structural health monitoring system, the output signal of the fiber grating sensor for monitoring the strain value of the measuring point is linearly corresponding to the measured strain value. Due to the characteristic that the fiber grating sensor is easily influenced by temperature, the wavelength value output by normal sensor strain monitoring has larger correlation with the ambient temperature and has a periodic change rule. In addition, in the same space structure, the structural stress changes are also related due to the structural integrity. Therefore, for the fiber grating sensors arranged in the same monitoring structure, output data of the fiber grating sensors are statistically similar. Moreover, the abnormal points are often a few points, and various indexes of the data on statistics often belong to outlier data. According to the characteristic, the invention extracts the characteristic data related to the operation state of the sensor from the raw output signal of the sensor, and determines the comprehensive abnormal index of the sensor based on the characteristic data so as to diagnose the abnormal state of the sensor, thereby being feasible and easy to realize.
Fig. 1 is a flowchart illustrating an embodiment of a method for diagnosing an abnormality of a fiber grating sensor in a structural health monitoring system according to the present invention. As shown in fig. 1, this embodiment performs the fiber grating sensor abnormality diagnosis by the following method:
step 11: raw output signals of a plurality of sensors arranged in a monitoring structure in the same time period are acquired.
In this embodiment, the output signals of the plurality of sensors in the same monitoring structure have statistically similar regularity, and therefore, the sensor abnormality diagnosis is also performed for the plurality of sensors arranged in the same monitoring structure. In addition, in view of the fact that in the structural health monitoring system, the system record data is stored in the database, the data size is huge, a time period is set for the purpose of analyzing the state of the sensor, for example, a certain month or a certain number of months in the history is set, and then, the raw output signals of a plurality of sensors arranged in the same monitoring structure in the same time period are obtained.
When reading the original output signal, the database can be read circularly by contrasting the sensor number list, and the data of a single sensor in the set time period can be extracted. If the data of the sensors in the list cannot be inquired, the measuring point channel corresponding to the sensor is closed, and the sensor number with the closed channel is recorded and output so as to be convenient for checking with monitoring software.
Step 12: feature data reflecting the operation state of each sensor is extracted based on the raw output signal of each sensor.
The feature data reflecting the sensor operation state is preset, and its extraction method is also known. In this embodiment, the type and the number of the feature data are not particularly limited, and all the feature data that can be extracted from the raw output signal of the sensor and reflect the operation state of the sensor belong to the protection scope of this embodiment.
Step 13: based on the characteristic data of each sensor, a comprehensive abnormality index of each sensor is determined.
Step 14: an abnormal state of each sensor is diagnosed based on a known criterion and the integrated abnormality index of each sensor.
As described above, since the output data of a plurality of sensors of the same monitoring structure is statistically regular, the overall abnormality index of the sensors determined based on the characteristic data reflecting the operating states of the sensors is also significantly concentrated, and the off-center outlier data is abnormal data, and the corresponding sensor is abnormal. By setting criteria, outlier data in the comprehensive abnormality index can be identified, and the abnormal state of the sensor can be diagnosed.
By adopting the method, the fiber bragg grating sensor in the structural health monitoring system is subjected to abnormity diagnosis, the abnormity diagnosis of the sensor can be automatically carried out based on the characteristics of the signal data output by the sensor, training samples and priori knowledge are not needed, the abnormity automatic diagnosis is completed in a simple and easily-realized mode, the problem of large workload of manual inspection is solved, and the abnormity diagnosis work efficiency of the sensor is improved.
In other embodiments, since the raw output signal of the sensor contains a large amount of data, the acquired raw output signal is pre-processed, specifically compressed, to obtain compressed data with a reduced amount of data, in order to facilitate analysis and processing of subsequent data. As a preferred embodiment, the specific implementation process of compressing the original output signal is as follows:
firstly, data irrelevant to the abnormality diagnosis in the data is eliminated, and only sensor wavelength value data in an original output signal is reserved.
And then, acquiring wavelength value data according to a set sampling period to obtain sampled data.
In order to reduce the data size and thus the amount of calculation data, a sampling period is set, which can be determined according to the period of the effective signal of the sensor. For example, setting the sampling period to 1000s, the original output signal data can be scaled down to 1000 times.
Fig. 2 is a waveform diagram showing a raw output signal of a sensor in a specific example, and in fig. 2, the abscissa represents a sampling number and the ordinate represents a wavelength value. Fig. 3 is a data waveform diagram of the original output signal waveform diagram of fig. 2 after sampling according to a sampling period of 1000s, wherein the abscissa is a sampling number and the ordinate is a wavelength value.
And then, low-pass filtering the sampled data by adopting a low-pass filter to obtain compressed data.
The low-pass filter may be implemented by a structure in the prior art, for example, by performing low-pass filtering on the sampled data by using a third-order low-pass filter, and the filtered data is used as the compressed data after the data amount is reduced.
Fig. 4 is a waveform diagram of compressed data filtered from the waveform diagram of the sampled data of fig. 3. In fig. 4, the abscissa indicates a sample number, and the ordinate indicates a wavelength value. As is apparent from the waveform diagram of fig. 4, the amount of data is greatly reduced.
In some embodiments, the characteristic data reflecting the operation state of the sensor at least comprises time domain energy value data reflecting the fluctuation intensity of the signal output of the sensor and frequency domain principal component period data reflecting the action period of the main load of a measuring point where the sensor is located. In still other embodiments, the characterization data further includes one or more of signal length, signal standard deviation, signal amplitude, signal variance, and signal peak indicator.
The method comprehensively considers the diagnosis accuracy, the diagnosis processing timeliness and the characteristic efficacy of the characteristic data, and as a preferred implementation mode, four signal characteristics of signal length, signal standard deviation, time domain energy value and frequency domain principal component period are adopted as the characteristics reflecting the operation state of the sensor and used for diagnosing whether the sensor is abnormal or not. The following explains the method for acquiring the feature data corresponding to these four signals one by one.
In most structural health monitoring systems, threshold detection is often set in data recording and storage of the fiber grating sensor, abnormal values exceeding the threshold are not recorded in a database, or each piece of sampling information cannot be recorded in the database in detail due to channel errors, software errors and the like. When sensor data is extracted according to a set time period, the lengths of data samples which are finally extracted in the database are very likely to be inconsistent and are almost certainly inconsistent for different sensors. Therefore, the length of the raw output signal finally extracted by each sensor is one of the important criteria for judging whether the sensor is normal. The signal length of the original output signal is denoted by symbol L, and the length of the signal can be obtained by using the prior art, for example, the length of the signal timing sequence is counted to obtain the number of data points included in the signal sequence.
The standard deviation can reflect the dispersion degree of the data value measured by the sensor and can be used as the basis for stabilizing the sensor signal. For a raw output signal of length N, the standard deviation Φ can be calculated using the following equation:
where N is 1,2, …, N, x (N) is the data value in the original output signal, i.e. the wavelength value data,
is the average of all data values in the original output signal.
The time domain energy value can reflect the signal output fluctuation intensity, and has a good indication effect on an abnormal sensor without obvious change in output. In order to avoid the influence caused by the abnormally jumped data, compressed data obtained by compressing the original output signal of the sensor is used as data for calculating the time-domain energy value. In addition, to reduce the data magnitude, the data is first de-averaged and the time domain energy value is then calculated.
Specifically, compression processing is performed on the original output signal of the sensor, and compressed data with a reduced data amount is obtained. Preferably, the method of obtaining the compressed data shown in fig. 4 is adopted, the sensor wavelength value data is extracted from the original data signal, the wavelength value data is collected according to the set sampling period, the sampled data is obtained, and finally the sampled data is low-pass filtered to obtain the compressed data.
Then, an average value of the compressed data is calculated
That is to say,
is the average of all data values in the compressed data.
Then, a compressed data-dequantized data value x' (n) is calculated:
n＝1,2,…,l 1 ，l 1 for compressing the length of data, x * And (n) is a data value in the compressed data.
And finally, calculating time domain energy value data E according to a time domain energy value calculation formula:
and the frequency domain principal component period reflects the action period of the sensor corresponding to the main load of the measuring point. The main load reflected by the fiber grating sensor which normally works is the ambient temperature load, and the load changes periodically at a time of about one day. In order to avoid the influence caused by the data of abnormal jump, compressed data obtained by compressing the original output signal of the sensor is used as data for calculating the frequency domain principal component period. The method for obtaining the compressed data may adopt a method for obtaining the compressed data when the time domain energy value data is calculated.
Then, fourier transform is performed on the compressed data, obtaining the spectral function of the signal:
in the formula
l 2 For the length of the compressed data, x (a) is the data value in the compressed data. In the process, in order to reduce the calculation amount, fast Fourier transform is selected to obtain a frequency spectrum, and normalization is performed. Fig. 5 shows a frequency domain characteristic diagram obtained after fourier transform of the compressed data waveform diagram of fig. 4. In fig. 5, the abscissa represents the period, and the ordinate represents the amplitude value.
Then, traversing the spectrum function, searching the maximum value max (X (k)), determining the maximum value as the frequency principal component, and obtaining the frequency k corresponding to the maximum value max 。
And converting the period T corresponding to the frequency principal component according to the following formula, and determining the period T as frequency domain principal component period data:
wherein the period unit is hours.
In other embodiments, to avoid interference from dc signals, the compressed data is first de-averaged and then fourier transformed. The method of de-equalization may refer to a data de-equalization processing mode when calculating the time domain energy value data.
In the actual processing process, for the case of a plurality of sensors, four feature data of one sensor are obtained and then stored in the feature list. And then, extracting and storing the characteristic data of the next sensor until the characteristic data of all the sensors are extracted and stored.
After the characteristic data of each sensor is obtained, the comprehensive abnormality index of each sensor is determined. As a preferred embodiment, determining the comprehensive abnormality index of the sensor according to the characteristic data of the sensor specifically includes:
and taking all the characteristic data of each sensor as a row of the matrix, and taking the same type of characteristic data of all the sensors as a column of the matrix to form the characteristic matrix.
Determining a one-dimensional feature aggregation center Y for each column in a feature matrix i1 (center); i1 ═ 1,2, …, p; p is the number of feature data.
Calculating the standard deviation of the features of each column in the feature matrix
m is the total number of all sensors, i2 is 1,2, …, m, Y i1 (i2) For the signature data value at column i1 at row i2,
is the average of all the characteristic data values in column i1, Y i1 Is the i1 th characteristic data. In calculating the standard deviation of features, in order to make each feature deviate from the index with a common weight, the deviation distance is divided by the standard deviation of the feature for processing.
Determining a composite anomaly index d (i2) for each sensor:
taking four signal characteristics of signal length L, signal standard deviation phi, time domain energy value E and frequency domain principal component period T as characteristics reflecting the running state of the sensor as an example, the process of determining the comprehensive abnormality index of the sensor is further explained.
For a specific example of four feature data, the number of feature data is 4, that is, p is 4. If the total number of all the sensors is m, taking all the characteristic data of each sensor as a row of the matrix, taking the characteristic data of the same type of all the sensors as a column of the matrix, and forming a characteristic matrix with m rows and 4 columns:
in practical applications, most sensors are in normal working condition, and the state lists are in an aggregation state in spatial distribution. Few sensors are abnormal, and their status characteristics tend to show a deviation from the characteristic aggregation center, with a more severe deviation meaning more severe sensor damage. In order to avoid the influence of the excessive deviation value on the data center, the one-dimensional feature aggregation center Y of each row in the feature matrix is preferably determined by adopting a mode of searching the point closest to the aggregation center as an aggregation center point through cyclic iteration i1 (center)。
The feature aggregation center Y of the first column of the feature matrix with respect to the signal length is determined 1 (center) as an example, the determination process is described:
first, the convergence factor for this class of features is defined as:
in the formula, b is a multiple of the convergence coefficient, and the distance close to the aggregation point can be judged by adjusting the size of b. The value range of b can be (0.5, 1.5).
Is the average of all m signal length eigenvalues in the first column of the eigenvalue matrix.
Then, the Euclidean distance between the two points is calculated, and the calculation formula is as follows: d (i, j) ═ l (i) -l (j) |. i, j ∈ (1,2, … m).
If D (i, j) < s (l), it is considered that the ith value is gathered around the jth value.
And (3) performing loop iteration, calculating a gathering value near each value in the first column of the feature matrix, and further obtaining the number of gathering points near each point L (i), wherein the number is marked as num [ L (i)]. Looking for num [ L (i)]The maximum value of (1), i.e., the point at which the number of nearby gathering points is the greatest, is labeled max [ L (i)]. Will point max [ L (i)]Corresponding signal length data value L (max [ L (i))]) Defined as the one-dimensional feature aggregation center of the feature L. I.e. Y 1 (center)＝L(max[L(i)])。
If there are a plurality of points with the maximum numerical value, the average value of the signal length data values corresponding to the plurality of points is taken as the one-dimensional feature aggregation center.
By adopting the same processing mode, the one-dimensional feature aggregation centers of other three feature data can be obtained, which are respectively: one-dimensional feature aggregation center Y of signal standard deviation feature phi 2 (center), one-dimensional feature aggregation center Y of time-domain energy value feature E 3 (center), one-dimensional feature aggregation center Y of frequency domain principal component periodic feature T 4 (center)。
And calculating the characteristic standard deviation of each column in the characteristic matrix, and determining the abnormality index of each sensor with four characteristic data according to a comprehensive abnormality index formula.
In other embodiments, it is considered that when the sensor is stuck, the data is always a certain value, and the extraction principal component period result is null. For a sensor with a null principal component period, replacing a null value with 0 and marking as a fault; meanwhile, the comprehensive abnormality index is + 3.
After the composite abnormality index for each sensor is determined, a diagnosis of whether the sensor is abnormal will be made based on known decisions. In some other preferred embodiments, diagnosing the abnormal state of each sensor based on the known criteria and the composite abnormality index of each sensor includes:
determining the mean value of the abnormality indexes from the integrated abnormality indexes d (i2) of all the sensors
If the comprehensive abnormality index d (i2) of the i2 th sensor meets the following conditions:
the i2 th sensor is determined to be in a fault state.
If the comprehensive abnormality index d of the i2 th sensor (i2)Satisfies the following conditions:
the i2 th sensor is determined to be in an abnormal state.
If the comprehensive abnormality index d (i2) of the i2 th sensor meets the following conditions:
the i2 th sensor is determined to be in a normal state.
Wherein, the coefficient k is determined 1 、k 2 Are all positive numbers greater than 1, and k 1 ＞k 2 。k 1 、k 2 The specific value of (c) can be adjusted according to practical application.
In one embodiment, the fiber grating sensors of 416 in a structural health monitoring system are analyzed, the four characteristic data are used, and a schematic diagram of the comprehensive abnormality index of each sensor is determined as shown in fig. 6. In fig. 6, the abscissa represents the sensor number, and the ordinate represents the comprehensive abnormality index. In fig. 6, the failure point and the abnormal point are also denoted by different symbols.
If the sensor is identified as being in a fault state or an abnormal state, the sensor in the fault state and/or the abnormal state can be subjected to field inspection with clear pertinence.
In other preferred embodiments, if the sensor is identified as a fault state, the fault type of the sensor can be preliminarily determined by analyzing the characteristic data of the sensor in the fault state.
Specifically, an average value of target characteristic data of a plurality of sensors and the target characteristic data value of a sensor in a failure state are acquired. For example, if the target feature data is time-domain energy value data, an average value of the time-domain energy value data of all the sensors and the time-domain energy value data of the sensor in the fault state are obtained.
Then, the target characteristic data value of the sensor in the failure state is compared with the average value of the target characteristic data to obtain a comparison result.
And determining the fault type of the sensor in the fault state according to the comparison result and the corresponding relation between the known fault type and the comparison result.
In a specific example, the comparison result is represented as too small and too large, and when four signal data of the signal length L, the signal standard deviation phi, the time domain energy value E and the frequency domain principal component period T are used as the characteristic data, the corresponding relationship between the fault type and the comparison result is shown in the following table.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (4)
1. A method for diagnosing anomalies in a fiber grating sensor in a structural health monitoring system, the method comprising:
acquiring original output signals of a plurality of sensors arranged in a monitoring structure in the same time period;
extracting feature data reflecting an operating state of each sensor based on the raw output signal of each sensor;
determining a comprehensive abnormality index of each sensor based on the characteristic data of each sensor;
diagnosing an abnormal state of each sensor based on known criteria and the composite abnormality index for each sensor;
the determining of the comprehensive abnormality index of each sensor based on the characteristic data of each sensor specifically includes:
taking all the characteristic data of each sensor as a row of a matrix, and taking the same type of characteristic data of all the sensors as columns of the matrix to form a characteristic matrix;
determining a one-dimensional feature aggregation center Y for each column in the feature matrix i1 (center); i1 ═ 1,2, …, p; p is the number of the characteristic data;
calculating a feature standard deviation for each column in the feature matrix
m is the total number of all sensors, i2 is 1,2, …, m, Y i1 (i2) For the signature data value at column i1 at row i2,
is the average of all the characteristic data values in column i1, Y i1 The ith 1 feature data;
determining a composite anomaly index d (i2) for each sensor:
2. the method as claimed in claim 1, wherein the one-dimensional feature aggregation center Y of each row in the feature matrix is determined by searching a point closest to the aggregation center as an aggregation center point through iterative loop i1 (center)。
3. The method for diagnosing the abnormality of the fiber grating sensor in the structural health monitoring system according to claim 1, wherein the diagnosing the abnormal state of each sensor according to the known criterion and the comprehensive abnormality index of each sensor specifically comprises:
the comprehensive abnormality index according to all sensorsd (i2) determining the mean value of the abnormality index
If the composite abnormality index d (i2) of the i2 th sensor satisfies:
determining that the i2 th sensor is in a fault state;
if the comprehensive abnormality index d (i2) of the i2 th sensor satisfies:
determining that the i2 th sensor is in an abnormal state;
if the comprehensive abnormality index d (i2) of the i2 th sensor satisfies:
judging that the i2 th sensor is in a normal state;
wherein, the coefficient k is determined 1 、k 2 Are all positive numbers greater than 1, and k 1 >k 2 。
4. The method for diagnosing abnormality of fiber grating sensor in structural health monitoring system according to claim 3, further comprising:
acquiring an average value of target characteristic data of a plurality of sensors and the target characteristic data value of the sensor in the fault state, and comparing the target characteristic data value of the sensor in the fault state with the average value of the target characteristic data to obtain a comparison result;
and determining the fault type of the sensor in the fault state according to the comparison result and the corresponding relation between the known fault type and the comparison result.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210803033.4A CN115265609A (en) | 2021-03-12 | 2021-03-12 | Method for diagnosing state of sensor in structural health monitoring system |
| CN202110272056.2A CN113029242B (en) | 2021-03-12 | 2021-03-12 | Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110272056.2A CN113029242B (en) | 2021-03-12 | 2021-03-12 | Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210803033.4A Division CN115265609A (en) | 2021-03-12 | 2021-03-12 | Method for diagnosing state of sensor in structural health monitoring system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113029242A CN113029242A (en) | 2021-06-25 |
| CN113029242B true CN113029242B (en) | 2022-09-02 |
Family
ID=76470432
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110272056.2A Active CN113029242B (en) | 2021-03-12 | 2021-03-12 | Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system |
| CN202210803033.4A Pending CN115265609A (en) | 2021-03-12 | 2021-03-12 | Method for diagnosing state of sensor in structural health monitoring system |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210803033.4A Pending CN115265609A (en) | 2021-03-12 | 2021-03-12 | Method for diagnosing state of sensor in structural health monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN113029242B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238604A (en) * | 2011-08-18 | 2011-11-09 | 无锡儒安科技有限公司 | Wireless sensor network failure diagnosis method |
| CN111474475A (en) * | 2020-03-22 | 2020-07-31 | 华南理工大学 | Motor fault diagnosis system and method |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106569160B (en) * | 2016-09-26 | 2019-11-12 | 株洲中车时代电气股份有限公司 | AuCT output voltage sensor method for diagnosing faults and fault tolerant control method |
| KR20180046746A (en) * | 2016-10-28 | 2018-05-09 | 삼성에스디에스 주식회사 | Method and Apparatus for Anomaly Detection |
| CN108507117A (en) * | 2017-10-13 | 2018-09-07 | 上海智容睿盛智能科技有限公司 | A kind of Air-conditioning system sensor method for diagnosing faults based on wavelet neural network |
| CN109871000A (en) * | 2019-02-25 | 2019-06-11 | 山东科技大学 | A kind of closed loop industrial process sensor method for diagnosing faults of data-driven |
| CN110119397B (en) * | 2019-04-18 | 2023-06-30 | 东南大学 | Deep learning method for simultaneously realizing data anomaly detection and data compression |
| CN110243497A (en) * | 2019-05-29 | 2019-09-17 | 北京暖云科技有限公司 | A kind of sensor fault diagnosis method and system based on principal component analysis |
| CN110319957B (en) * | 2019-06-25 | 2020-12-22 | 哈尔滨工程大学 | Fault diagnosis method for irregular abnormal value of sensor of ship structure stress monitoring system |
| CN112098600A (en) * | 2020-09-14 | 2020-12-18 | 哈尔滨工业大学 | Fault detection and diagnosis method for chemical sensor array |
| CN112414446B (en) * | 2020-11-02 | 2023-01-17 | 南昌智能新能源汽车研究院 | Data-driven transmission sensor fault diagnosis method |
| CN112213640B (en) * | 2020-11-17 | 2024-01-26 | 润电能源科学技术有限公司 | Motor fault diagnosis method and related equipment thereof |
-
2021
- 2021-03-12 CN CN202110272056.2A patent/CN113029242B/en active Active
- 2021-03-12 CN CN202210803033.4A patent/CN115265609A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238604A (en) * | 2011-08-18 | 2011-11-09 | 无锡儒安科技有限公司 | Wireless sensor network failure diagnosis method |
| CN111474475A (en) * | 2020-03-22 | 2020-07-31 | 华南理工大学 | Motor fault diagnosis system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113029242A (en) | 2021-06-25 |
| CN115265609A (en) | 2022-11-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108388860B (en) | Aero-engine rolling bearing fault diagnosis method based on power entropy spectrum-random forest | |
| CN102265227B (en) | Method and apparatus for creating state estimation models in machine condition monitoring | |
| JP5101396B2 (en) | Soundness diagnosis method and program, and windmill soundness diagnosis device | |
| CN116756595B (en) | Conductive slip ring fault data acquisition and monitoring method | |
| CN111914883A (en) | Spindle bearing state evaluation method and device based on deep fusion network | |
| CN115409131B (en) | Production line abnormity detection method based on SPC process control system | |
| JP4148524B2 (en) | System and method for evaluating correlation | |
| CN114781467A (en) | Fault detection method and device based on vibration similarity | |
| CN112668526A (en) | Bolt group loosening positioning monitoring method based on deep learning and piezoelectric active sensing | |
| CN111881594B (en) | Non-stationary signal state monitoring method and system for nuclear power equipment | |
| CN110553770B (en) | Fault diagnosis method for large drift abnormal value of sensor of ship structure stress monitoring system | |
| CN115935286A (en) | Abnormal point detection method, device and terminal for railway bearing state monitoring data | |
| CN116306806A (en) | Fault diagnosis model determining method and device and nonvolatile storage medium | |
| CN113029242B (en) | Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system | |
| CN111316075A (en) | Plant growth analysis device | |
| CN116248176B (en) | Optical fiber state monitoring and early warning method, system, equipment and medium | |
| CN115659271A (en) | Sensor abnormality detection method, model training method, system, device, and medium | |
| CN110553807B (en) | Open-circuit fault diagnosis method for sensor of ship structure stress monitoring system | |
| CN113672658A (en) | Power equipment online monitoring error data identification method based on multiple correlation coefficients | |
| CN112114215A (en) | Transformer aging evaluation method and system based on error back propagation algorithm | |
| CN114492636B (en) | Transformer winding state signal acquisition system | |
| CN117172139B (en) | Performance test method and system for copper-clad aluminum alloy cable for communication | |
| CN117421616B (en) | Mine shaft detection system and method | |
| CN116660761B (en) | Lithium ion battery detection method and system | |
| KR102504421B1 (en) | Vibration measurement error determination method and vibration error determination system using the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |