CN110568082A - cable wire breakage distinguishing method based on acoustic emission signals - Google Patents
cable wire breakage distinguishing method based on acoustic emission signals Download PDFInfo
- Publication number
- CN110568082A CN110568082A CN201910821280.5A CN201910821280A CN110568082A CN 110568082 A CN110568082 A CN 110568082A CN 201910821280 A CN201910821280 A CN 201910821280A CN 110568082 A CN110568082 A CN 110568082A
- Authority
- CN
- China
- Prior art keywords
- acoustic emission
- cable
- time
- input
- output
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
- G01N29/4427—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with stored values, e.g. threshold values
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4481—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Immunology (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- Acoustics & Sound (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a method for judging broken wires of a cable based on an acoustic emission signal, and belongs to the technical field of nondestructive testing. The invention respectively fixes a plurality of sensors on a cable, and simultaneously obtains the waveform of an acoustic emission signal received by each sensor; carrying out time-frequency analysis on each group of broken wire acoustic emission signals to obtain a time-frequency graph of each group of broken wire acoustic emission signals; performing continuous wavelet transformation on the acoustic emission signals, and selecting a scale and a time range according to a preset intensity threshold to obtain a time-frequency graph of the acoustic emission signals with the size of k multiplied by p; using a local mean value method to perform down-sampling on the time-frequency image, and forming an image with the size of m multiplied by m by using all mean values; inputting the mxm image as input data into an automatic encoder model to obtain corresponding output data; calculating a reconstruction error between the input data and the output data of the auto-encoder; and comparing the reconstruction error with a discrimination threshold value to judge whether the obtained acoustic emission signal is a broken wire acoustic emission signal or not, so as to realize discrimination of the cable wire breakage phenomenon.
Description
Technical Field
the invention relates to a method for judging broken wires of a cable based on the construction and classification of an automatic encoder of an acoustic emission signal, which is used for monitoring whether the cable of a cable-stayed bridge has the phenomenon of broken wires and belongs to the technical field of nondestructive testing.
Background
the cable is used as a core bearing part of a cable-stayed bridge, the quality of the cable is directly related to the safety of the bridge, and the quality of the cable is mainly influenced by the breakage degree of the steel wires in the cable. Therefore, in order to ensure the safety and reliability of the cable-stayed bridge, the cable is subjected to factors such as dynamic load, environmental corrosion, stress corrosion and fatigue for a long time during the operation of the bridge, and all the factors can cause the steel wires in the cable to break during the use process. And the acoustic emission technology is adopted to effectively judge the cable wire breakage phenomenon.
The current acoustic emission damage judgment method is mainly based on a characteristic parameter method or a modal analysis method. The characteristic parameter method has limited analysis capability on acoustic emission signals due to less used information amount, and the change of external conditions easily influences the judgment result. Although the accuracy of the modal analysis method is high, different models must be established manually for different materials and structures. Therefore, it is necessary to provide a discrimination algorithm with higher adaptability while ensuring higher accuracy.
Disclosure of Invention
in order to solve the problems of low algorithm recognition rate, low adaptability and the like in the conventional cable wire breakage judgment method, the invention discloses a cable wire breakage judgment method based on an acoustic emission signal, which mainly solves the technical problems that: the automatic encoder based on the acoustic emission signal time-frequency diagram realizes the discrimination of the cable wire breakage phenomenon, can identify the hidden characteristics of the acoustic emission signal without manual characteristic extraction in the discrimination process, and has the advantages of accurate discrimination, strong adaptability and the like.
the purpose of the invention is realized by the following technical scheme.
The invention discloses a method for judging broken wires of a cable based on acoustic emission signals. And carrying out time-frequency analysis on each group of broken wire acoustic emission signals to obtain a time-frequency graph of each group of broken wire acoustic emission signals. Performing continuous wavelet transformation on the acoustic emission signals, and selecting a scale and a time range according to a preset intensity threshold to obtain a time-frequency graph of the acoustic emission signals with the size of k multiplied by p; and (3) performing down-sampling on the time-frequency image by using a local mean value method, and forming an image with the size of m multiplied by m by using all mean values. The m × m image is input as input data to the automatic encoder model, and corresponding output data is obtained. A reconstruction error between the input data and the output data of the auto-encoder is calculated. And comparing the reconstruction error with a discrimination threshold value to judge whether the obtained acoustic emission signal is a broken wire acoustic emission signal or not, so as to realize discrimination of the cable wire breakage phenomenon.
the invention discloses a method for judging broken wires of a cable based on acoustic emission signals, which comprises the following steps:
the method comprises the following steps that firstly, two or more acoustic emission sensors are respectively fixed on a cable, when a broken wire acoustic emission phenomenon occurs in the cable, the waveforms of acoustic emission signals received by the sensors are simultaneously obtained, and broken wire acoustic emission signals received by every two adjacent sensors serve as a group of broken wire acoustic emission signals.
And secondly, performing time-frequency analysis on the groups of broken wire acoustic emission signals acquired in the first step to respectively obtain a time-frequency graph of the groups of broken wire acoustic emission signals, wherein for each data point in the time-frequency graph, the abscissa corresponds to the frequency of the point, the ordinate corresponds to the time of the point, and the value of the point corresponds to the frequency and the energy density of the signal component at the time. Performing continuous wavelet transformation on the acoustic emission signals acquired in the step one, and selecting a scale and a time range according to a preset acoustic emission signal intensity threshold to obtain a time-frequency graph of the acoustic emission signals with the size of k multiplied by p; and (3) performing down-sampling on the obtained time-frequency image of the acoustic emission signal by using a local mean value method, wherein the down-sampling method is to equally divide the time-frequency image into a plurality of sub-blocks, calculate the mean value of all points in each sub-block, and form an image with the size of m multiplied by m by using all the mean values. And calculating the reconstruction error between the input data constructed in the step two and the output data of the automatic encoder obtained in the step three.
And step three, inputting the time-frequency diagram with the size of m multiplied by m constructed in the step two into the constructed automatic encoder model as input data to obtain corresponding output data.
Step three, the automatic encoder is a neural network model comprising an input layer, a single-layer hidden layer and an output layer, the output layer and the input layer of the network have the same dimensionality, and the neural network model construction method comprises the following steps:
step 3.1: acquiring a large number of acoustic emission signals generated under the condition of no wire breakage in the cable according to the waveform acquisition method of the acoustic emission signals in the first step, processing the signals by adopting the time-frequency analysis method in the second step, taking the obtained data as training data, and establishing a training data set; the acoustic emission signals generated under the condition of non-broken wires comprise friction among steel wires in the cable and impact on the cable.
Step 3.2: determining the structure of an automatic encoder model and constructing a corresponding objective function for the automatic encoder model;
The dimension of the input layer of the automatic encoder is m, the dimension of the hidden layer is n, and the dimension of the output layer is m. Defining the input vector as x, the hidden layer output as h, and the output vector as y, i.e.
x＝[x1,x2,…,xm]
h＝[h1,h2,…,hn]
y＝[y1,y2,…,ym]
Let W be the weight of the input layer to the hidden layer, W 'be the weight of the hidden layer to the output layer, b be the bias on the hidden layer, and b' be the bias on the output layer, i.e.
b＝[b1,b2,…,bn]
b′＝[b′1,b′2,…,b′m]
Then there is
f (x) in the formula is an activation function, and a sigmoid function is usually used, and the expression is
The formula for the objective function of the auto-encoder is:
wherein the content of the first and second substances,
Wherein:
n is the number of training samples in each batch during training,
x(k)for the input vector corresponding to the kth sample in the current training batch,
y(k)output direction corresponding to the kth sample in the current training batchThe amount of the compound (A) is,
is the average liveness of the jth neuron over the current training batch,
βrAnd rho are constants;
step 3.3: and (3) training the automatic encoder model constructed in the step (3.2) by using a training data set, and updating the weight and the bias parameters in the automatic encoder model until the value of the target function is reduced to be within an allowable range.
And step four, calculating the reconstruction error between the input data constructed in the step two and the output data of the automatic encoder obtained in the step three.
the calculation formula of the reconstruction error in the step four is as follows:
wherein:
x is the input vector corresponding to the input data constructed in the step two,
y is the output vector corresponding to the output data of the automatic encoder obtained in the step three,
m is the dimension of the input and output layer;
and step five, comparing the reconstruction error obtained in the step four with a judgment threshold value, and judging whether the obtained acoustic emission signal is a broken wire acoustic emission signal or not, so as to realize the judgment of the cable wire breakage phenomenon.
The method for acquiring the discrimination threshold comprises the following steps: calculating the reconstruction errors between the input and the output of all training samples in the training set according to the step four, and defining the average value of the obtained reconstruction errors as Lfif the discrimination threshold is 3Lf. Defining the loss function value corresponding to the current input as L, and then determining the rule as:
L<3Lfthe current signal is a friction signal; l is more than or equal to 3Lfthe current signal is a wire break signal.
has the advantages that:
1. the invention discloses a method for judging broken cable wires based on acoustic emission signals, which is a method for judging broken cable wires based on the construction and classification of an automatic encoder of the acoustic emission signals.
2. Because the automatic encoder can learn the characteristics of the input sample automatically, the time-frequency diagram of the acoustic emission signal generated by friction can be used as a training sample to train the automatic encoder, and the automatic encoder can find the internal rule of the friction signal. For a trained automatic encoder, a friction signal is used as an input, the obtained output can better reproduce the input, and the target function of the automatic encoder is a smaller value, which indicates that the value of the loss function is smaller. However, in the case of a wire break signal or a crack propagation signal as input, the value of the loss function will be relatively large because it does not have an intrinsic law similar to the friction signal. According to the method for judging the cable wire breakage based on the acoustic emission signal, the cable wire breakage phenomenon is judged based on the acoustic emission signal time-frequency diagram automatic encoder, the hidden characteristics of the acoustic emission signal can be identified without manual characteristic extraction in the judging process, the identification accuracy is improved compared with the existing characteristic parameter method, and the adaptability of the algorithm is enhanced compared with the existing modal analysis method.
Drawings
FIG. 1 is a flow chart of a method for discriminating broken wire of a cable based on the construction and classification of an automatic encoder of an acoustic emission signal, which is disclosed by the invention;
fig. 2 is a network configuration diagram of the automatic encoder.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
example 1
as shown in fig. 1, the method for discriminating a broken cable wire based on the construction and classification of an automatic encoder for acoustic emission signals disclosed in this embodiment specifically includes the following steps:
Step one, selecting a resonance type acoustic emission sensor with a measurement range of 20 kHz-400 kHz and sensitivity of 75 dB-115 dB. The acoustic emission sensor is fixed at the end point of the cable to be measured by a clamp, a coupling agent is coated on the contact surface of the sensor and the cable to supply power to the sensor, the output signal of the sensor is converted into digital quantity at the sampling rate of 10MSPS after band-pass filtering, and the digital quantity is transmitted to a computer to be read by a user program. And if the user program reads that the output voltage of the sensor exceeds 0.1V, the user program considers that the acoustic emission phenomenon is generated in the cable, and stores the signals in the previous 0.026s time period and the next 0.052s time period of the current time. This way the complete waveform of the acoustic emission signal within the cable is acquired.
Secondly, performing time-frequency analysis on the acoustic emission signals acquired in the first step to obtain a time-frequency graph of the acquired acoustic emission signals, wherein for each point in the time-frequency graph, the abscissa corresponds to the frequency of the point, the ordinate corresponds to the time of the point, and the value of the point corresponds to the frequency and the energy density of the signal component at the time; and preprocessing the time-frequency diagram.
The second step is realized by the following concrete method:
Step 2.1: performing continuous wavelet transformation on the acoustic emission signals acquired in the step one, and selecting a proper scale and time range to obtain a time-frequency diagram of the acoustic emission signals with the size of 1900 x 40000;
step 2.2: using a local mean value method to perform downsampling on the time-frequency graph of the acoustic emission signal obtained in the step 2.1, wherein the method is to equally divide the time-frequency graph into sub-blocks, calculate the mean value of all points in each sub-block, and form an image with the size of 20 x 20 by using all the mean values;
Step 2.3: and (3) after logarithm processing is carried out on all pixel points of the time-frequency image after the down-sampling obtained in the step (2.2), normalization processing is carried out.
And step three, inputting the time-frequency diagram constructed in the step two into the constructed automatic encoder model as input data to obtain output data of the automatic encoder.
The automatic encoder is a neural network model comprising an input layer, a single-layer hidden layer and an output layer, the output layer and the input layer of the network have the same dimension, and the construction process comprises the following steps:
Step 3.1: acquiring a large number of acoustic emission signals generated under the condition that the wire in the cable is not broken (such as friction between steel wires in the cable, impact on the cable and the like) according to the signal acquisition method in the first step, processing the signals by adopting the preprocessing method in the second step to obtain input data of the automatic encoder as training data and verification data, and establishing a training data set with the size of 7000 and a verification data set with the size of 3000;
step 3.2: an autoencoder model is constructed, the structure of which is shown in fig. 2, defining the dimension m of the input layer of the autoencoder as 400, the dimension n of the hidden layer as 500, and the dimension m of the output layer as 400. Defining the input vector as x, the hidden layer output as h, and the output vector as y, i.e.
x＝[x1,x2,…,xm]
h＝[h1,h2,…,hn]
y＝[y1,y2,…,ym]
let W be the weight of the input layer to the hidden layer, W 'be the weight of the hidden layer to the output layer, b be the bias on the hidden layer, and b' be the bias on the output layer, i.e.
b＝[b1,b2,…,bn]
b′＝[b′1,b′2,…,b′m]
Then there is
F (x) in the formula is an activation function, and a sigmoid function is usually used, and the expression is
the formula for the objective function of the auto-encoder is:
Wherein the content of the first and second substances,
Wherein:
n is the number of training samples per batch during training, which in this embodiment is 50,
x(k)for the input vector corresponding to the kth sample in the current training batch,
y(k)the output vector corresponding to the kth sample in the current training batch,
is the average liveness of the jth neuron over the current training batch,
βrAnd ρ are constants, 10 and 0.01, respectively, in this example;
Step 3.3: training the automatic encoder model by using a training data set, and optimizing and updating weights and bias parameters in the automatic encoder model by adopting an Adam-based algorithm in the training process until the value of a target function is reduced to be within 30;
and step four, calculating the reconstruction error between the input data constructed in the step two and the output data of the automatic encoder obtained in the step three.
The calculation formula of the reconstruction error in the step four is as follows:
The symbols in the formula have the following meanings:
x is the input vector corresponding to the input data constructed in the step two,
y is the output vector corresponding to the output data of the automatic encoder obtained in the step three,
m is the dimension of the input-output layer, which in this embodiment is 400;
and step five, comparing the reconstruction error obtained in the step four with a judgment threshold value, and judging whether the obtained acoustic emission signal is a broken wire acoustic emission signal.
The method for acquiring the discrimination threshold comprises the following steps: calculating the reconstruction error between the input and the output of all training samples in the training set according to the calculation formula in the fourth step, and defining the average value of the obtained reconstruction errors as LfIf the discrimination threshold is 3Lf。
and (4) identifying the actual broken wire acoustic emission signal by using the method for distinguishing the broken wire of the cable based on the acoustic emission signal from the first step to the fifth step.
In the present embodiment, the mounting pitches of the sensors are 12m and 35m, respectively. In order to control the position of the breaking point, the steel wire is scored at a designated position by using an angle grinder. When the mounting pitch of the sensor is 12m, the notch positions are at distances of 6m, 8m, and 10m from the sensor 1, respectively; when the mounting pitch of the sensor is 35m, the notch positions are at 30m and 33m from the sensor 1, respectively. The acoustic emission detection system designed and manufactured is used for collecting the wire breakage signals, and a large amount of friction, crack propagation and steel wire breakage signals are obtained.
the obtained signals are processed and statistically calculated according to the above-mentioned identification steps, and the final identification effects of the different signals are shown in table 1. From table 1, it can be known that the recognition rate of the broken wire signal is above 80%, wherein the recognition rate of the acoustic emission signal at the moment of breakage reaches 100%, and the recognition requirement can be met.
TABLE 1 statistical results of filament breakage identification
the above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. a method for discriminating broken wires of a cable based on acoustic emission signals is characterized in that: comprises the following steps of (a) carrying out,
Firstly, respectively fixing two or more acoustic emission sensors on a cable, and when a broken wire acoustic emission phenomenon occurs in the cable, simultaneously acquiring the waveform of an acoustic emission signal received by each sensor, wherein the broken wire acoustic emission signals received by every two adjacent sensors are used as a group of broken wire acoustic emission signals;
Secondly, performing time-frequency analysis on each group of broken wire acoustic emission signals acquired in the first step to respectively obtain a time-frequency graph of each group of broken wire acoustic emission signals, wherein for each data point in the time-frequency graph, the abscissa corresponds to the frequency of the point, the ordinate corresponds to the time of the point, and the value of the point corresponds to the frequency and the magnitude of the energy density of the signal component at the time; performing continuous wavelet transformation on the acoustic emission signals acquired in the step one, and selecting a scale and a time range according to a preset acoustic emission signal intensity threshold to obtain a time-frequency graph of the acoustic emission signals with the size of k multiplied by p; using a local mean value method to carry out down-sampling on the obtained time-frequency image of the acoustic emission signal, wherein the down-sampling method is to equally divide the time-frequency image into a plurality of sub-blocks, calculate the mean value of all points in each sub-block, and form an image with the size of m multiplied by m by using all the mean values; calculating a reconstruction error between the input data constructed in the second step and the output data of the automatic encoder obtained in the third step;
step three, inputting the time-frequency diagram with the size of m multiplied by m constructed in the step two into the constructed automatic encoder model as input data to obtain corresponding output data;
Step four, calculating the reconstruction error between the input data constructed in the step two and the output data of the automatic encoder obtained in the step three;
And step five, comparing the reconstruction error obtained in the step four with a judgment threshold value, and judging whether the obtained acoustic emission signal is a broken wire acoustic emission signal or not, so as to realize the judgment of the cable wire breakage phenomenon.
2. The method for discriminating the broken wire of the cable based on the acoustic emission signal as claimed in claim 1, wherein: step three, the automatic encoder is a neural network model comprising an input layer, a single-layer hidden layer and an output layer, the output layer and the input layer of the network have the same dimension, the neural network model is constructed by the following method,
Step 3.1: acquiring a large number of acoustic emission signals generated under the condition of no wire breakage in the cable according to the waveform acquisition method of the acoustic emission signals in the first step, processing the signals by adopting the time-frequency analysis method in the second step, taking the obtained data as training data, and establishing a training data set; the acoustic emission signals generated under the condition of non-broken wires comprise friction among steel wires in the cable and impact on the cable;
step 3.2: determining the structure of an automatic encoder model and constructing a corresponding objective function for the automatic encoder model;
the dimension of an input layer of the automatic encoder is m, the dimension of a hidden layer is n, and the dimension of an output layer of the automatic encoder is m; defining the input vector as x, the hidden layer output as h, and the output vector as y, i.e.
x＝[x1,x2,…,xm]
h＝[h1,h2,…,hn]
y＝[y1,y2,…,ym]
let W be the weight of the input layer to the hidden layer, W 'be the weight of the hidden layer to the output layer, b be the bias on the hidden layer, and b' be the bias on the output layer, i.e.
b＝[b1,b2,…,bn]
b′＝[b′1,b′2,…,b′m]
Then there is
F (x) in the formula is an activation function, and a sigmoid function is usually used, and the expression is
The formula for the objective function of the auto-encoder is:
Wherein the content of the first and second substances,
Wherein:
n is the number of training samples in each batch during training,
x(k)for the input vector corresponding to the kth sample in the current training batch,
y(k)the output vector corresponding to the kth sample in the current training batch,
is the average liveness of the jth neuron over the current training batch,
βrAnd rho are constants;
step 3.3: and (3) training the automatic encoder model constructed in the step (3.2) by using a training data set, and updating the weight and the bias parameters in the automatic encoder model until the value of the target function is reduced to be within an allowable range.
3. A method for discriminating cable broken wire based on acoustic emission signal as claimed in claim 1 or 2, characterized in that: the calculation formula of the reconstruction error in the step four is as follows,
Wherein:
x is the input vector corresponding to the input data constructed in the step two,
y is the output vector corresponding to the output data of the automatic encoder obtained in the step three,
and m is the dimension of the input and output layer.
4. the method as claimed in claim 3, wherein the method comprises determining whether the cable is broken based on the acoustic emission signalthe method is characterized in that: step five, the method for obtaining the discrimination threshold value comprises the steps of calculating the reconstruction errors between the input and the output of all the training samples in the training set according to the step four, and defining the average value of the obtained reconstruction errors as Lfif the discrimination threshold is 3Lf(ii) a Defining the loss function value corresponding to the current input as L, and then determining the rule as:
L<3Lfthe current signal is a friction signal; l is more than or equal to 3Lfthe current signal is a wire break signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910821280.5A CN110568082A (en) | 2019-09-02 | 2019-09-02 | cable wire breakage distinguishing method based on acoustic emission signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910821280.5A CN110568082A (en) | 2019-09-02 | 2019-09-02 | cable wire breakage distinguishing method based on acoustic emission signals |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110568082A true CN110568082A (en) | 2019-12-13 |
Family
ID=68777260
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910821280.5A Pending CN110568082A (en) | 2019-09-02 | 2019-09-02 | cable wire breakage distinguishing method based on acoustic emission signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110568082A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111521687A (en) * | 2020-05-09 | 2020-08-11 | 山东大学 | Inhaul cable broken wire distinguishing method and system based on acoustic emission signal analysis |
| CN112198232A (en) * | 2020-09-14 | 2021-01-08 | 昆明理工大学 | Drainage pipeline working condition detection and identification method |
| CN113237957A (en) * | 2021-05-31 | 2021-08-10 | 郑州大学 | Acoustic emission-based parallel steel wire inhaul cable damage space positioning algorithm |
| CN114595733A (en) * | 2022-05-10 | 2022-06-07 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-term and short-term memory network |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4979124A (en) * | 1988-10-05 | 1990-12-18 | Cornell Research Foundation | Adaptive, neural-based signal processor |
| EP0907078A1 (en) * | 1997-10-01 | 1999-04-07 | ROLLS-ROYCE plc | An apparatus and a method for locating a source of acoustic emissions in an article |
| CN106248801A (en) * | 2016-09-06 | 2016-12-21 | 哈尔滨工业大学 | A kind of Rail crack detection method based on many acoustie emission events probability |
| CN107589180A (en) * | 2017-10-11 | 2018-01-16 | 大连理工大学 | A kind of Prestressed concrete cylinder pipe fracture of wire optical fiber acoustic emission monitoring system and its monitoring method |
| CN107884475A (en) * | 2017-10-18 | 2018-04-06 | 常州大学 | A kind of city gas pipeline method for diagnosing faults based on deep learning neutral net |
| CN108445084A (en) * | 2018-02-09 | 2018-08-24 | 深圳市市政设计研究院有限公司 | A kind of fracture of wire monitoring method and system based on drag-line or sunpender |
| CN109490814A (en) * | 2018-09-07 | 2019-03-19 | 广西电网有限责任公司电力科学研究院 | Metering automation terminal fault diagnostic method based on deep learning and Support Vector data description |
| CN109583475A (en) * | 2018-11-02 | 2019-04-05 | 阿里巴巴集团控股有限公司 | The monitoring method and device of exception information |
| CN110044554A (en) * | 2019-04-16 | 2019-07-23 | 重庆大学 | A kind of online test method of the metal pressure container leakage based on acoustic emission signal |
-
2019
- 2019-09-02 CN CN201910821280.5A patent/CN110568082A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4979124A (en) * | 1988-10-05 | 1990-12-18 | Cornell Research Foundation | Adaptive, neural-based signal processor |
| EP0907078A1 (en) * | 1997-10-01 | 1999-04-07 | ROLLS-ROYCE plc | An apparatus and a method for locating a source of acoustic emissions in an article |
| CN106248801A (en) * | 2016-09-06 | 2016-12-21 | 哈尔滨工业大学 | A kind of Rail crack detection method based on many acoustie emission events probability |
| CN107589180A (en) * | 2017-10-11 | 2018-01-16 | 大连理工大学 | A kind of Prestressed concrete cylinder pipe fracture of wire optical fiber acoustic emission monitoring system and its monitoring method |
| CN107884475A (en) * | 2017-10-18 | 2018-04-06 | 常州大学 | A kind of city gas pipeline method for diagnosing faults based on deep learning neutral net |
| CN108445084A (en) * | 2018-02-09 | 2018-08-24 | 深圳市市政设计研究院有限公司 | A kind of fracture of wire monitoring method and system based on drag-line or sunpender |
| CN109490814A (en) * | 2018-09-07 | 2019-03-19 | 广西电网有限责任公司电力科学研究院 | Metering automation terminal fault diagnostic method based on deep learning and Support Vector data description |
| CN109583475A (en) * | 2018-11-02 | 2019-04-05 | 阿里巴巴集团控股有限公司 | The monitoring method and device of exception information |
| CN110044554A (en) * | 2019-04-16 | 2019-07-23 | 重庆大学 | A kind of online test method of the metal pressure container leakage based on acoustic emission signal |
Non-Patent Citations (4)
| Title |
|---|
| NATHALIE JAPKOWICZ等: "A Novelty Detection Approach to Classification", 《PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE》 * |
| R. J. DIENDER: "Acoustic emission (AE) as a structural health monitoring (SHM) method for stay cables", 《DELFT UNIVERSITY OF TECHNOLOGY》 * |
| 张鹏: "《基于Java 3D的虚拟现实技术研究与实践》", 31 August 2017, 苏州大学出版社 * |
| 王攀等: "《优化与控制中的软技术方法研究》", 31 January 2017, 湖北科学技术出版社 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111521687A (en) * | 2020-05-09 | 2020-08-11 | 山东大学 | Inhaul cable broken wire distinguishing method and system based on acoustic emission signal analysis |
| CN111521687B (en) * | 2020-05-09 | 2021-09-28 | 山东大学 | Inhaul cable broken wire distinguishing method and system based on acoustic emission signal analysis |
| CN112198232A (en) * | 2020-09-14 | 2021-01-08 | 昆明理工大学 | Drainage pipeline working condition detection and identification method |
| CN113237957A (en) * | 2021-05-31 | 2021-08-10 | 郑州大学 | Acoustic emission-based parallel steel wire inhaul cable damage space positioning algorithm |
| CN113237957B (en) * | 2021-05-31 | 2023-09-29 | 郑州大学 | Acoustic emission-based parallel steel wire inhaul cable damage space positioning algorithm |
| CN114595733A (en) * | 2022-05-10 | 2022-06-07 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-term and short-term memory network |
| CN114595733B (en) * | 2022-05-10 | 2024-04-02 | 山东大学 | Bridge inhaul cable broken wire signal identification method and system based on long-short-term memory network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110568082A (en) | cable wire breakage distinguishing method based on acoustic emission signals | |
| JP5145417B2 (en) | Signal determination method, signal determination apparatus, program, and signal determination system | |
| CN110514745B (en) | Method for determining broken wire position of cable based on multi-frequency acoustic emission signals | |
| CN110082429B (en) | Tunnel lining nondestructive testing auxiliary judgment method combining machine learning | |
| CN111310686B (en) | Steel wire rope broken wire damage identification method of IPSO-BP neural network | |
| CN113340353B (en) | Monitoring method, equipment and medium for power transmission line | |
| CN111062093B (en) | Automobile tire service life prediction method based on image processing and machine learning | |
| CN114878582B (en) | Defect detection and analysis method and system for special steel | |
| CN114926462A (en) | Intelligent detection method and system for metal material surface defects | |
| CN111721834A (en) | Cable partial discharge online monitoring defect identification method | |
| CN113657217A (en) | Concrete state recognition model based on improved BP neural network | |
| CN116519699A (en) | Structural damage detection system and structural damage detection method | |
| CN116990229B (en) | Defect detection method and system for copper plating layer surface of product | |
| CN116881530B (en) | Device surface defect detection system based on deep learning | |
| CN113341283A (en) | Transformer bushing partial discharge mode identification method | |
| CN117191956A (en) | Acoustic emission-based titanium alloy stress corrosion damage classification method and apparatus | |
| CN114972353B (en) | Quality evaluation method and system for metal high-pressure special pipe fitting | |
| CN112241610B (en) | Health state assessment method of crosslinked polyethylene cable | |
| CN112069621B (en) | Method for predicting residual service life of rolling bearing based on linear reliability index | |
| CN111189906B (en) | On-line intelligent judging and classifying identification method for alternating current magnetic field defects | |
| CN114679216A (en) | Algorithm and device for improving analysis precision based on OTDR | |
| CN114549403A (en) | Multi-monomer intelligent precise geometric circle center detection method for side section of mechanical part | |
| CN113919396A (en) | Vibration signal and image characteristic machine tool cutter wear state monitoring method based on semi-supervised learning | |
| CN114186586A (en) | Damage identification method and equipment based on two-dimensional convolutional neural network | |
| CN111311591A (en) | Method for detecting lifting amount of high-speed railway contact net |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191213 |