CN110715929B - Distributed strain micro crack detection system and method based on stacking self-encoder - Google Patents

Distributed strain micro crack detection system and method based on stacking self-encoder Download PDF

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CN110715929B
CN110715929B CN201910973652.6A CN201910973652A CN110715929B CN 110715929 B CN110715929 B CN 110715929B CN 201910973652 A CN201910973652 A CN 201910973652A CN 110715929 B CN110715929 B CN 110715929B
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CN110715929A (en
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宋青松
武金睿
陈禹
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Changan University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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    • G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge

Abstract

The invention discloses a distributed strain crack detection system and method based on a stacked self-encoder, which take crack detection as a two-classification problem by utilizing good characteristic representation capability of a deep neural network, construct the deep neural network based on the stacked self-encoder and realize the classification of cracks and non-cracks of a strain subsequence of a structural body. The method can accurately and inexhaustibly detect the tiny crack with the opening width of 32 mu m on the steel beam in the laboratory, and provides a solution with good noise robustness for the detection of the distributed strain crack on the surface of the structure.

Description

Distributed strain micro crack detection system and method based on stacking self-encoder
Technical Field
The invention belongs to the field of pattern recognition, and particularly relates to a distributed strain micro crack detection system and method based on a stacked self-encoder.
Background
Crack detection has been an important issue in the field of structural health monitoring. The crack detection method comprises a manual observation method and a nondestructive detection method. The manual observation method needs special maintenance personnel to use a professional tool to perform periodic inspection, and is low in efficiency and strong in subjectivity. The nondestructive testing method is mainly used for testing the structural body cracks through data obtained by ultrasonic waves, X rays, ground penetrating radars, cameras and the like. These sensors are all point-to-point sensors, and cannot measure the entire data of the structure, and cracks are easily missed.
Disclosure of Invention
The invention aims to provide a distributed strain micro crack detection system and method based on a stacked self-encoder, so as to overcome the defects in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a stacked self-encoder based distributed strain microcrack detection system, comprising: the strain sequence acquisition module is used for acquiring distributed strain on the surface of the structure; a strain sequence preprocessing module: the strain sequences obtained by collection are subjected to z-score standardization and are intercepted into strain subsequences; the self-learning and characterization module of the characteristics based on the stacking self-encoder comprises the following modules: extracting features of the divided strain subsequences; the Softmax classification and identification module is used for carrying out secondary classification on the extracted subsequence characteristics and judging the probability that each subsequence belongs to a crack subsequence and a non-crack subsequence;
further, the strain sequence acquisition module: laying an optical fiber sensor on the surface of a structure, and collecting distributed strain on the surface of the structure by using a distributed optical fiber sensing system based on BOTDA; the strain sequence preprocessing module comprises:
further, a z-score normalization module that normalizes the strain sequence to data of 0 means 1 standard deviation and a sliding window intercept module. The sliding window module cuts the normalized strain sequence into a set of strain subsequences with the length of 21 through a sliding window with the length of 21 and the step length of 1. The self-learning and characterization module of the characteristics based on the stacking self-encoder comprises the following modules: the system is composed of 3 automatic encoder modules, and the encoding parts of the 3 automatic encoder modules are used as characteristic representations.
Further, the self-learning and characterization module based on the characteristics of the stacked self-encoder: composed of 3 automatic encoder modules for extracting the characteristics of the divided strain subsequences, wherein the automatic encoder modules are used for inputting data x, characteristics h and outputting
Figure BDA0002232923400000021
The relationship between can be expressed as fθ(. and g)θ'Two functions, specifically as follows:
h＝fθ(x)＝sf(Wx+bh)
Figure BDA0002232923400000022
wherein W and bhRespectively, a connection matrix and an offset vector, W, between the input data x and the feature hTAnd bvRespectively characteristic h and output
Figure BDA0002232923400000023
A connection matrix and an offset vector between, WTIs a transposition of WAnd (4) matrix. sfAnd sgIs an activation function.
The process of the feature self-learning and characterization module based on the stacked self-encoder is specifically as follows:
hi＝sf(Wixi+bh,i)
Figure BDA0002232923400000024
wherein x isi，hi，
Figure BDA0002232923400000025
Input data x, characteristic h and output of the i (i ═ 1,2, …, n) th autoencoder module, respectively
Figure BDA0002232923400000031
WiAnd Wi TTo which a weight matrix is connected, bh,iAnd bv,iIs its offset vector, xi＝hi-1，sfAnd sgIs an activation function.
A distributed strain crack detection method based on a stacked self-encoder comprises the following steps:
step 1: strain sequence collection;
step 2: standardizing the acquired strain sequence by using z-score, intercepting the strain sequence by using a sliding window with the length of 21 and the step length of 1 to obtain a strain subsequence, and marking the strain subsequence according to the intercepted position;
and step 3: automatically learning features characterizing the strain subsequence using a stacked self-encoder based neural network;
and 4, step 4: performing secondary classification on the extracted characteristics of the strain subsequence by adopting a Softmax classifier to finish crack detection;
further, the strain sequence acquisition in step 1 specifically comprises the following steps: the optical fiber sensor is adhered to the surface of the structure body through epoxy resin, two ends of the optical fiber are connected to a BOTDA-based distributed optical fiber sensing system, the BOTDA-based distributed optical fiber sensing system measures the Brillouin frequency shift of the optical fiber through two light sources, namely pumping light and detection light, and the distributed strain of the surface of the structure body is obtained through the linear relation between the Brillouin frequency shift and the strain.
Further, the specific process of the strain sequence processing in the step 2 is as follows:
step 2.1: the mean of the collected strain sequences was subtracted by their standard deviations, and the data were obtained as 0 mean 1 standard deviation.
Step 2.2: sliding a sliding window with the length of 21 and the step size of 1 along the acquired strain sequence is used, and the strain sequence is intercepted into a group of strain subsequences with the length of 21.
Step 2.3: marking the obtained strain subsequences as cut position mark labels, marking the strain subsequences which are cut by taking the crack as the center as crack subsequences, marking the strain subsequences at the left side and the right side of the strain subsequences as crack subsequences, and marking the rest strain subsequences as non-crack subsequences.
Further, the specific process of using the neural network based on the stacked self-encoder to automatically learn and characterize the strain subsequence in step 3 is as follows:
step 3.1: initializing the model, and determining the number of layers and the number of neurons of the model. And randomly initializing a connection weight matrix and a bias vector in the model. The number of neurons in the input layer is equal to 21, which is the length of the strained subsequence.
Step 3.2: pre-training a stacked self-encoder, the stacked self-encoder consisting of 3 auto-encoders, each auto-encoder being pre-trained with the resulting strain sub-sequence. The loss function of the pre-trained automatic encoder is the mean square error between the input and the output, and specifically is as follows:
Figure BDA0002232923400000041
wherein x is an input strain subsequence,
Figure BDA0002232923400000042
for the reconstructed data output from the autoencoder, M is the number of all the input strain subsequences, Xm、
Figure BDA0002232923400000043
Respectively the mth strain subsequence of the input model and the corresponding mth subsequence of the output reconstruction.
Further, in step 4, a Softmax classifier is adopted to classify the sub-sequences of the variables, and the specific method is as follows:
step 4.1: constructing a Softmax classifier using a hypothesis function h for a given input zδ(z) for each class l, a probability value p (y ═ l | z) is estimated, l ∈ {0,1}, assuming a function hδ(z) outputting a vector of dimensions t representing the probability values of the t estimates, t being 2, assuming the function hδ(z) is as follows:
Figure BDA0002232923400000044
wherein, delta1,δ2Are all parameters of the Softmax classifier,
Figure BDA0002232923400000045
z(i)to input, y(i)For output, the probability of the Softmax classifier classifying z into class l is:
Figure BDA0002232923400000051
wherein z is(i)To input, y(i)Is an output;
step 4.2: pre-training Sofmax, inputting the strain subsequence into the pre-trained stacked self-encoder to obtain the output characteristic z(i)In z is(i)And its label category y(i)Pre-training Softmax, wherein a loss function is a cross entropy function, and the method comprises the following specific steps:
Figure BDA0002232923400000052
wherein the content of the first and second substances,
Figure BDA0002232923400000053
for all the parameters of the Softmax classifier,
Figure BDA0002232923400000054
is the class probability of the output, λ1The weight coefficient is a weight coefficient connecting a weight matrix and a bias vector regular term in Softmax, M is the total number of input strain subsequences, and K is the category number and is 2.
Step 4.3: and fine adjustment, namely stacking an encoding part of the self-encoder and then connecting a Softmax classifier, so that the self-encoder has a classification function. And utilizing the pre-trained strain subsequence to fine tune a connection weight matrix and an offset vector of the coding part of the stacked self-encoder and the overall structure of the Softmax classifier. The loss function during trimming is a cross loss function, and specifically, the loss function is as follows:
Figure BDA0002232923400000055
where ω is the connected weight matrix and offset vector in the stacked self-encoder, and Θ is ω and δ, λ2Weight coefficients connecting the weight matrix and the regular term of the bias vector in the stacked self-encoder are used.
Step 4.4: the Softmax classifier receives as its input the features stacked from the encoder output, outputs class 0 or 1 of the strain subsequence, 0 representing non-crack, 1 representing crack; for feature z of stacked self-encoder output(i)Selecting the probability p (y)(i)＝l|z(i)(ii) a δ) the largest class i as the class to which the feature corresponds. The distributed strain subsequences are reduced to the truncated position and the adjacently positioned fracture subsequences are merged into one fracture.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention realizes data acquisition by the distributed optical fiber sensor and changes the traditional point-to-point sensing mode. The differences between different data are reduced by normalization. Meanwhile, the contradiction between high spatial resolution and low signal-to-noise ratio of the distributed optical fiber sensor is overcome by a method based on a stacked self-encoder. Stacked self-encoders can extract highly robust, discernable features for classification in data with low signal-to-noise ratio. The crack detection device has the advantages that the crack detection device is remarkable in crack detection, can detect the micro cracks, and is improved in the detection effect of the micro cracks.
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FIG. 1 is a schematic flow diagram of the system of the present invention;
FIG. 2 is a schematic diagram of an autoencoder in the present invention;
FIG. 3 is a schematic diagram of a stacked self-encoder in the present invention;
FIG. 4 is a process schematic of the method of the present invention;
FIG. 5 is a diagram illustrating pre-training and fine-tuning in accordance with the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1 to 5, a distributed strain crack detection system based on a stacked self-encoder includes a strain sequence acquisition module; a strain sequence preprocessing module; a feature self-learning and characterization module based on the stacked self-encoder; and (3) a Softmax classification identification module (the specific flow is shown in figure 1).
The strain sequence acquisition module is used for acquiring the distributed strain of the structural body, and the acquired distributed strain of the structural body is a one-dimensional sequence;
the strain sequence preprocessing module comprises: a z-score normalization module that normalizes strain sequences to 0-mean 1 standard deviation data and a sliding window module. The sliding window module cuts the normalized strain sequence into a set of strain subsequences with the length of 21 through a sliding window with the length of 21 and the step length of 1. Marking the obtained strain subsequences as cut position mark labels, marking the strain subsequences which are cut by taking the crack as the center as crack subsequences, marking the strain subsequences at the left side and the right side of the strain subsequences as crack subsequences, and marking the rest strain subsequences as non-crack subsequences.
The self-learning and characterization module based on the characteristics of the stacked self-encoder comprises: 3 autoencoder modules As shown in FIG. 2, the autoencoder modules for input data x, feature h and output
Figure BDA0002232923400000071
The relationship between can be expressed as fθ(. and g)θ'Two functions, specifically as follows:
h＝fθ(x)＝sf(Wx+bh)
Figure BDA0002232923400000072
wherein W and bhRespectively, a connection matrix and an offset vector, W, between the input data x and the feature hTAnd bvRespectively characteristic h and output
Figure BDA0002232923400000073
A connection matrix and an offset vector between, WTIs a transposed matrix of W. sfAnd sgIs an activation function.
The process of the feature self-learning and characterization module based on the stacked self-encoder is specifically as follows:
hi＝sf(Wixi+bh,i)
Figure BDA0002232923400000074
wherein x isi，hi，
Figure BDA0002232923400000075
Input data x, characteristic h and output of the i (i ═ 1,2, …, n) th autoencoder module, respectively
Figure BDA0002232923400000076
WiAnd Wi TTo connect it withWeight matrix, bh,iAnd bv,iIs its offset vector, xi＝hi-1，sfAnd sgIs an activation function.
The method for classifying the transformer subsequence by adopting the Softmax classifier comprises the following steps:
constructing a Softmax classifier using a hypothesis function h for a given input zδ(z) for each class l, a probability value p (y ═ l | z) is estimated, l ∈ {0,1}, assuming a function hδ(z) outputting a vector of dimensions t representing the probability values of the t estimates, t being 2, assuming the function hδ(z) is as follows:
Figure BDA0002232923400000077
wherein, delta1,δ2Are all parameters of the Softmax classifier,
Figure BDA0002232923400000081
z(i)to input, y(i)For output, the probability of the Softmax classifier classifying z into class l is:
Figure BDA0002232923400000082
wherein z is(i)To input, y(i)Is an output;
the Softmax classifier receives as its input the features stacked from the encoder output, outputs class 0 or 1 of the strain subsequence, 0 representing non-crack, 1 representing crack; for feature z of stacked self-encoder output(i)Selecting the probability p (y)(i)＝l|z(i)(ii) a δ) the largest class i as the class to which the feature corresponds. The distributed strain subsequences are reduced to the truncated position and the adjacently positioned fracture subsequences are merged into one fracture.
A distributed strain crack detection method based on a stacked self-encoder comprises the following specific steps as shown in FIG. 5:
1) strain sequence acquisition;
2) standardizing the collected strain sequence by using z-score, intercepting the strain sequence by using a sliding window with the length of 21 and the step length of 1 to obtain a strain subsequence, and marking the strain subsequence according to the intercepted position;
2.1: the mean of the collected strain sequences was subtracted by their variance to obtain 0-mean-1 standard deviation data.
2.2: sliding a sliding window with the length of 21 and the step size of 1 along the acquired strain sequence is used, and the strain sequence is intercepted into a group of strain subsequences with the length of 21.
2.3: marking the obtained strain subsequences as cut position mark labels, marking the strain subsequences which are cut by taking the crack as the center as crack subsequences, and marking the strain subsequences at the left side and the right side of the strain subsequences as the crack subsequences.
3) Automatically learning features characterizing the strain subsequence with a neural network based on a stacked self-encoder;
3.1: initializing the model, and determining the number of layers and the number of neurons of the model. And randomly initializing a connection weight matrix and a bias vector in the model. The number of neurons in the input layer is equal to 21, which is the length of the strained subsequence.
3.2: pre-training a stacked self-encoder, the stacked self-encoder consisting of 3 auto-encoders, each auto-encoder being pre-trained with the resulting strain sub-sequence. The loss function of the pre-trained automatic encoder is the mean square error between the input and the output, and specifically is as follows:
Figure BDA0002232923400000091
wherein x is an input strain subsequence,
Figure BDA0002232923400000092
for the reconstructed data output from the autoencoder, M is the number of all the input strain subsequences, Xm、
Figure BDA0002232923400000093
Respectively the mth strain subsequence of the input model and the corresponding mth subsequence of the output reconstruction.
4) Secondly, performing secondary classification on the extracted characteristics of the strain subsequence by adopting a Softmax classifier to finish crack detection;
4.1: constructing a Softmax classifier using a hypothesis function h for a given input zδ(z) for each class l, a probability value p (y ═ l | z) is estimated, l ∈ {0,1}, assuming a function hδ(z) outputting a vector of dimensions t representing the probability values of the t estimates, t being 2, assuming the function hδ(z) is as follows:
Figure BDA0002232923400000094
wherein, delta1,δ2Are all parameters of the Softmax classifier,
Figure BDA0002232923400000095
z(i)to input, y(i)For output, the probability of the Softmax classifier classifying z into class l is:
Figure BDA0002232923400000101
wherein z is(i)To input, y(i)Is an output;
4.2: pre-training Sofmax, inputting the strain subsequence into the pre-trained stacked self-encoder to obtain the output characteristic z(i)In z is(i)And its label category y(i)Pre-training Softmax, wherein a loss function is a cross entropy function, and the method comprises the following specific steps:
Figure BDA0002232923400000102
wherein the content of the first and second substances,
Figure BDA0002232923400000103
for all the parameters of the Softmax classifier,
Figure BDA0002232923400000104
is the class probability of the output, λ1The weight coefficient is a weight coefficient connecting a weight matrix and a bias vector regular term in Softmax, M is the total number of input strain subsequences, and K is the category number and is 2.
4.3: and fine adjustment, namely stacking an encoding part of the self-encoder and then connecting a Softmax classifier, so that the self-encoder has a classification function. And utilizing the pre-trained strain subsequence to fine tune a connection weight matrix and an offset vector of the coding part of the stacked self-encoder and the overall structure of the Softmax classifier. The loss function during trimming is a cross loss function, and specifically, the loss function is as follows:
Figure BDA0002232923400000105
where ω is the connected weight matrix and offset vector in the stacked self-encoder, and Θ is ω and δ, λ2Weight coefficients connecting the weight matrix and the regular term of the bias vector in the stacked self-encoder are used.
4.4: the Softmax classifier receives as its input the features stacked from the encoder output, outputs class 0 or 1 of the strain subsequence, 0 representing non-crack, 1 representing crack; for feature z of stacked self-encoder output(i)Selecting the probability p (y)(i)＝l|z(i)(ii) a δ) the largest class i as the class to which the feature corresponds. The distributed strain subsequences are reduced to the truncated position and the adjacently positioned fracture subsequences are merged into one fracture.
Effects of the implementation
The fiber optic sensor is first pre-tensioned and then adhered to the steel structure surface by epoxy. And two ends of the optical fiber sensor are connected with a distributed optical fiber sensing system based on BOTDA, and distributed strain data of the surface of the structural body distributed along the radial direction of the optical fiber sensor are obtained. By adopting the method for detecting the micro cracks based on the stacking self-encoder, the micro cracks with the opening width of 32 mu m can be accurately and uninterruptedly detected based on the acquired distributed strain data, and the method is an effective method for detecting the micro cracks on the surface of the steel structure in a distributed manner.

Claims (5)

1. A distributed strain microcrack detection system based on stacked self-encoders, comprising:
strain sequence acquisition module: the system is used for acquiring distributed strain of the surface of the structure; the strain sequence acquisition module specifically comprises: laying an optical fiber sensor on the surface of a structure, and collecting distributed strain on the surface of the structure by using a distributed optical fiber sensing system based on BOTDA;
a strain sequence preprocessing module: the strain acquisition device is used for performing z-score standardization on the acquired distributed strain and intercepting the distributed strain into a strain subsequence; the strain sequence pre-processing module comprises a z-score normalization module and a sliding window module: the z-score normalization module normalizes the strain sequences to 0-mean 1 standard deviation data; the sliding window module intercepts a group of strain subsequences with the lengths of 21 from the normalized strain sequence through a sliding window with the length of 21 and the step length of 1;
the self-learning and characterization module of the characteristics based on the stacking self-encoder comprises the following modules: the system comprises 3 automatic encoder modules, a data processing module and a data processing module, wherein the automatic encoder modules are used for extracting the characteristics of the divided strain subsequences, inputting the characteristics into the strain subsequences and outputting the characteristics into the strain subsequences;
and the Softmax classification and identification module is used for judging the probability that each strain subsequence belongs to the crack subsequence and the non-crack subsequence so as to carry out secondary classification on the extracted characteristics of the strain subsequences.
2. The distributed strain microcrack detection system according to claim 1, wherein the self-learning and characterization module based on the self-encoder comprises 3 autoencoder modules for extracting the characteristics of the divided strain subsequences, and the autoencoder modules input data x, characteristics h and output data x, h and output data h
Figure FDA0003436420230000011
The relationship between is represented as fθ(. and g)θ'Two functions, specifically as follows:
h＝fθ(x)＝sf(Wx+bh)
Figure FDA0003436420230000012
wherein W and bhRespectively, a connection matrix and an offset vector, W, between the input data x and the feature hTAnd bvRespectively characteristic h and output
Figure FDA0003436420230000021
A connection matrix and an offset vector between, WTIs a transposed matrix of W, sfAnd sgIs an activation function;
the process of the feature self-learning and characterization module based on the stacked self-encoder is specifically as follows:
hi＝sf(Wixi+bh,i)
Figure FDA0003436420230000022
wherein x isi，hi，
Figure FDA0003436420230000023
Input data x, characteristic h and output of the ith automatic encoder module respectively
Figure FDA0003436420230000024
And i is 1,2, L, n, WiAnd Wi TTo which a weight matrix is connected, bh,iAnd bv,iIs its offset vector, xi＝hi-1，sfAnd sgIs an activation function.
3. A distributed strain micro crack detection method based on a stacked self-encoder is characterized by comprising the following steps:
step 1: collecting distributed strain on the surface of the structure; the strain sequence acquisition specific process comprises the following steps: the method comprises the following steps that an optical fiber sensor is adhered to the surface of a structure body through epoxy resin, two ends of an optical fiber are connected to a BOTDA-based distributed optical fiber sensing system, the BOTDA-based distributed optical fiber sensing system measures Brillouin frequency shift of the optical fiber through two light source pumping light and detection light, and distributed strain of the surface of the structure body is obtained through the linear relation between the Brillouin frequency shift and strain;
step 2: carrying out z-score standardization on the acquired distributed strain and intercepting the distributed strain into a strain subsequence; the method specifically comprises the following steps:
step 2.1: subtracting the mean value of the acquired strain sequence, and dividing the mean value by the standard deviation to obtain data of 0 mean value 1 standard deviation;
step 2.2: intercepting the normalized strain sequence into a group of strain subsequences with the length of 21 by using a sliding window with the length of 21 and the step size of 1;
step 2.3: marking the obtained strain subsequences as cut position mark labels, marking the strain subsequences which are cut by taking the crack as the center as crack subsequences, marking 3 strain subsequences on the left side and 4 strain subsequences on the right side as crack subsequences, and marking the rest as non-crack subsequences;
and step 3: automatically learning features characterizing the strain subsequence using a stacked self-encoder based neural network;
and 4, step 4: and (4) performing secondary classification on the extracted characteristics of the strain subsequence by adopting a Softmax classifier, and completing crack detection.
4. The distributed strain microcrack detection method based on the stacked self-encoder according to claim 3, wherein the specific process of using the neural network based on the stacked self-encoder to automatically learn and characterize the strain subsequence features in step 3 is as follows:
step 3.1: initializing a model, determining the number of layers and the number of neurons of the model, randomly initializing a connection weight matrix and a bias vector in the model, and inputting the number of the neurons of the layer to be equal to the length of a strain subsequence;
step 3.2: pre-training a stacked self-encoder, the stacked self-encoder consisting of 3 auto-encoders, pre-training each auto-encoder with the obtained strain sub-sequence, the loss function of the pre-trained auto-encoder being the mean square error L between input and output1The method comprises the following steps:
Figure FDA0003436420230000031
wherein x is an input strain subsequence,
Figure FDA0003436420230000032
for the reconstructed data output from the autoencoder, M is the number of all the input strain subsequences, Xm、
Figure FDA0003436420230000033
Respectively the mth strain subsequence of the input model and the corresponding mth subsequence of the output reconstruction.
5. The distributed strain micro crack detection method based on the stacked self-encoder as claimed in claim 3, wherein a Softmax classifier is adopted in the step 4 to realize classification of the strain subsequences, and the specific method is as follows:
step 4.1: constructing a Softmax classifier using a hypothesis function h for a given input zδ(z) for each class l, a probability value p (y ═ l | z) is estimated, l ∈ {0,1}, assuming a function hδ(z) outputting a vector of dimensions t representing the probability values of the t estimates, t being 2, assuming the function hδ(z) is as follows:
Figure FDA0003436420230000041
wherein the content of the first and second substances,
Figure FDA0003436420230000042
δ1,δ2is all the parameters of the Softmax classifier, z(i)To input, y(i)For output, the probability of the Softmax classifier classifying z into class l is:
Figure FDA0003436420230000043
wherein z is(i)To input, y(i)Is an output;
step 4.2: pre-training Sofmax, inputting the strain subsequence into the pre-trained stacked self-encoder to obtain the output characteristic z(i)In z is(i)And its label category y(i)Pre-training Softmax, wherein a loss function is a cross entropy function, and the method comprises the following specific steps:
Figure FDA0003436420230000044
wherein the content of the first and second substances,
Figure FDA0003436420230000045
is the class probability of the output, λ1The weight coefficient is a weight coefficient connecting a weight matrix and a bias vector regular term in Softmax, M is the total number of input strain subsequences, and K is the category number which is 2;
step 4.3: utilizing a pre-trained strain subsequence to fine-tune a connection weight matrix and a bias vector of an encoding part of the stacked self-encoder and an overall structure of the Softmax classifier, wherein a loss function during fine tuning is a cross loss function, and the method specifically comprises the following steps:
Figure FDA0003436420230000046
where ω is in a stacked self-encoderConnecting the weight matrix and the offset vector, theta is omega and delta, lambda2The weight coefficient is a weight coefficient which is connected with the weight matrix and the bias vector regular term in the stacked self-encoder;
step 4.4: the Softmax classifier receives as its input the features stacked from the encoder output, outputs class 0 or 1 of the strain subsequence, 0 representing non-crack, 1 representing crack; for feature z of stacked self-encoder output(i)Selecting the probability p (y)(i)＝l|z(i)(ii) a δ) the largest class i as the class to which the feature corresponds.
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