CN111982514A - Bearing fault diagnosis method based on semi-supervised deep belief network - Google Patents

Bearing fault diagnosis method based on semi-supervised deep belief network Download PDF

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CN111982514A
CN111982514A CN202010807298.2A CN202010807298A CN111982514A CN 111982514 A CN111982514 A CN 111982514A CN 202010807298 A CN202010807298 A CN 202010807298A CN 111982514 A CN111982514 A CN 111982514A
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CN111982514B (en
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叶楠
常佩泽
张露予
王嘉
李佳航
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Hebei University of Technology
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Abstract

The application provides a bearing fault diagnosis method based on a semi-supervised deep belief network, which comprises the following steps of: collecting vibration signal data; performing wavelet transformation noise reduction on the vibration signal data and completing reconstruction; classifying the reconstructed signal data according to the labels, and extracting corresponding time domain characteristics; screening the classified signal data according to a set rule, and dividing the screened data into a training data set and a test data set; inputting a training data set into a semi-supervised deep belief network, and carrying out deep training on the network; inputting the test data set into a semi-supervised deep belief network, and carrying out fault classification judgment on the data in the test data set through a deeply trained model. The beneficial effect of this application: the judged bearing fault can be directly output by inputting the working data, and the automation degree is higher; the method is more suitable for bearing fault diagnosis under multiple working conditions, has stronger compatibility and universality, and improves the bearing fault diagnosis precision.

Description

Bearing fault diagnosis method based on semi-supervised deep belief network
Technical Field
The disclosure relates to the field of semi-supervised deep belief network learning, in particular to a bearing fault diagnosis method based on a semi-supervised deep belief network.
Background
Aiming at bearing fault diagnosis, the conventional bearing fault diagnosis method cannot automatically extract features at present, and the features need to be manually extracted and are judged depending on expert knowledge; the intelligent bearing fault diagnosis method mostly focuses on a fault diagnosis model with single load and single rotating speed, can automatically classify the fault types of the bearings, and does not need to manually extract features.
The traditional bearing fault diagnosis method needs manual feature extraction, wastes time and labor, results after feature extraction cannot be automatically classified, expert knowledge is needed for judgment, the labor cost is further increased, and the efficiency is low; the intelligent bearing fault diagnosis algorithm trains the model aiming at single load and single rotating speed, so that the bearing fault diagnosis under the change of multiple working conditions cannot be adapted, the diagnosis precision is low, in addition, the method also needs a large amount of data with fault type labels, and the cost for manufacturing the label data is high.
Disclosure of Invention
The bearing fault diagnosis method based on the semi-supervised deep belief network aims to solve the problems.
In a first aspect, the application provides a bearing fault diagnosis method based on a semi-supervised deep belief network, which includes the following steps:
collecting vibration signal data;
performing wavelet transformation noise reduction on the vibration signal data and completing reconstruction;
classifying the reconstructed signal data according to the labels, and extracting corresponding time domain characteristics;
screening the classified signal data according to a set rule, and dividing the screened data into a training data set and a test data set;
inputting a training data set into a semi-supervised deep belief network, and carrying out deep training on the network;
inputting the test data set into a semi-supervised deep belief network, and carrying out fault classification judgment on the data in the test data set through a deeply trained model.
According to the technical scheme provided by the embodiment of the application, the wavelet transformation denoising and reconstruction of the vibration signal data are specifically performed, and the method specifically comprises the following steps:
considering the vibration signal data x (t) as a space V in a subspace of the function space0Space V0Can be decomposed into V1And W1I.e. V0＝V1+W1，V1Can be decomposed into V2And W2By analogy, Vj-1Can be decomposed into VjAnd WjJ is resolution, j is 0 or 1;
the signal decomposition and noise reduction process of the vibration signal data x (t) is as follows:
Figure BDA0002629601130000021
wherein
Figure BDA0002629601130000022
Is a linear combination weight with a resolution of 0, P0x (t) is x (t) at V0A smooth approximation of (i.e. a profile of x (t) at a resolution of 0, phi0k(t) is a scale function;
Figure BDA0002629601130000023
wherein
Figure BDA0002629601130000024
For linear combining weights at resolution 1, P1x (t) is x (t) at V1A smooth approximation of (i.e. a profile of x (t) at a resolution of 1, phi1k(t) is a scale function;
Figure BDA0002629601130000025
wherein
Figure BDA0002629601130000026
Discrete details at resolution 1, D1x (t) is x (t) in W1Projection of1k(t) is a wavelet function;
P0x(t)＝P1x(t)+D1x(t)；
Figure BDA0002629601130000027
Figure BDA0002629601130000028
the decomposed and denoised signals are respectively:
when j is equal to 0, the value of j,
Figure BDA0002629601130000031
when j is equal to 1, the value of j,
Figure BDA0002629601130000032
the reconstructed signal is:
Figure BDA0002629601130000033
according to the technical scheme provided by the embodiment of the application, the label comprises fault position information and damage size information of the bearing, the fault position information comprises inner ring faults, rolling body faults and outer ring faults, and the damage size information comprises 0.178mm, 0.356mm and 0.533 mm; the labels comprise ten types, wherein nine types are labels simultaneously containing fault position information and damage size information, and the tenth type is labels of the bearing health state.
According to the technical scheme provided by the embodiment of the application, the extracting the corresponding time domain feature specifically comprises: and extracting a mean value, a mean square value, a root mean square value, an average amplitude value, a kurtosis value, a peak-peak value, a standard deviation, a variance, a skewness value, a pulse factor, a skewness factor, a waveform factor, a kurtosis coefficient, a margin coefficient, a kurtosis factor and a waveform entropy from the reconstructed signal data to form a total data set.
According to the technical scheme provided by the embodiment of the application, the screening of the classified signal data according to the set rule specifically comprises:
the total data set was screened using the Maximum Mean Difference (MMD) algorithm, given two distributions s and t, the MMD between them is defined as:
Figure BDA0002629601130000034
where E is the expectation for the allocation,
Figure BDA0002629601130000035
to map raw data to a function of the Regenerated Kernel Hilbert Space (RKHS), when s and t distributions are the same, MMD2(s, t) ═ 0, and the kernel function associated with this map is k (x)s,xt)＝<φ(xs),φ(xt)>；
Figure BDA0002629601130000036
And
Figure BDA0002629601130000037
the empirical estimate of MMD, representing distribution s and distribution t, respectively, is as follows:
Figure BDA0002629601130000041
when the data is consistent with 0 being less than or equal to LM(Ds,Dt) If the condition is less than 0.16, the data is selected as the screened data, and if the condition is not met, the data is removed.
According to the technical scheme provided by the embodiment of the application, 80% of screened data is selected as a training set, 20% of the screened data is selected as a test set, 10% of the data in the training set is provided with a label, the rest 90% of the data in the training set is unlabeled data, and the label of the data in the training set is predicted and identified by a semi-supervised deep belief network.
According to the technical scheme provided by the embodiment of the application, the inputting of the training data set into the semi-supervised deep belief network to perform deep training on the network specifically comprises the following steps:
the semi-supervised deep belief network (SSDBN) is formed by stacking semi-supervised restricted Boltzmann machines (SSRBMs), the SSDBN is composed of an input layer, a plurality of hidden layers and an output layer, each layer is provided with a plurality of neurons, the neurons only have two states of activation and non-activation, the neurons of each layer are not connected with each other, the neurons in each layer are fully connected, information is transmitted by using an activation function, weight values are updated, and weight values are weighted, the SSRBMs are composed of a visible layer, a hidden layer and a supervision layer, and the energy function of the SSRBMs is defined as:
Figure BDA0002629601130000042
where v is a visible layer, h is a hidden layer, u is a supervisory layer, ψ ═ wij,pkj,ai,ck,bj) A is a visual layer bias value, b is a hidden layer bias value, c is a supervised layer bias value, λ is a weight parameter for controlling a ratio of supervised learning to unsupervised learning, w is a weight between the visual layer and the hidden layer, p is a weight between the supervised layers, and ψ is (w ═ isij，pkj，ai，ck，bj) And can be updated as:
Figure BDA0002629601130000044
wherein tau is iteration times, eta is learning rate;
the activation function connecting the layers of the SSRBM is the Isigmoid function:
Figure BDA0002629601130000051
wherein a is a threshold, α is a slope, and satisfies:
Figure BDA0002629601130000052
wherein alpha isminMinimum slope to work for the Isigmoid function.
According to the technical scheme provided by the embodiment of the application, the inputting of the test data set into the semi-supervised deep belief network and the fault classification and judgment of the data in the test data set through the deeply trained model specifically comprise: the semi-supervised deep belief network reserves the trained weight and bias matrix, the supervision layer refers to the label data to assist in predicting the label of the unlabelled data, the test data are transmitted from the input layer to the output layer according to the SSDBN after being input, and finally the test data are classified.
The invention has the beneficial effects that: the application provides a bearing fault diagnosis method based on a semi-supervised deep belief network, which can directly output the judged bearing fault by inputting working data and has higher automation degree; the method is more suitable for bearing fault diagnosis under multiple working conditions, has stronger compatibility and universality, and improves the bearing fault diagnosis precision; and the semi-supervised deep belief network needs a small amount of label data during model training, so that the training cost is reduced.
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FIG. 1 is a flow chart of a first embodiment of the present application;
fig. 2 is a diagram of a semi-supervised deep belief network in the first embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention, the following detailed description of the present invention is provided in conjunction with the accompanying drawings, and the description of the present section is only exemplary and explanatory, and should not be construed as limiting the scope of the present invention in any way.
Fig. 1 shows a flow chart of a first embodiment of the present application, which includes the following steps:
and S1, collecting vibration signal data.
In this embodiment, the acceleration sensor is provided in the outer ring portion of the bearing of the rotary machine, and the magnetic adsorption type acceleration sensor is selected when the vibration amplitude of the bearing is large, and the adhesive attachment type acceleration sensor is selected when the vibration amplitude of the bearing is small.
And S2, performing wavelet transformation denoising on the vibration signal data and completing reconstruction.
The method specifically comprises the following steps: considering the vibration signal data x (t) as a space V in a subspace of the function space0Space V0Can be decomposed into V1And W1I.e. V0＝V1+W1，V1Can be decomposed into V2And W2By analogy, Vj-1Can be decomposed into VjAnd WjJ is resolution, j is 0 or 1;
the signal decomposition and noise reduction process of the vibration signal data x (t) is as follows:
Figure BDA0002629601130000061
wherein
Figure BDA0002629601130000062
Is a linear combination weight with a resolution of 0, P0x (t) is x (t) at V0A smooth approximation of (i.e. a profile of x (t) at a resolution of 0, phi0k(t) is a scale function;
Figure BDA0002629601130000063
wherein
Figure BDA0002629601130000064
For linear combining weights at resolution 1, P1x (t) is x (t) at V1A smooth approximation of (i.e. a profile of x (t) at a resolution of 1, phi1k(t) is a scale function;
Figure BDA0002629601130000065
wherein
Figure BDA0002629601130000066
Discrete details at resolution 1, D1x (t) is x (t) in W1Projection of1k(t) is a wavelet function;
P0x(t)＝P1x(t)+D1x(t)
Figure BDA0002629601130000067
Figure BDA0002629601130000068
the decomposed and denoised signals are respectively:
when j is equal to 0, the value of j,
Figure BDA0002629601130000071
when j is equal to 1, the value of j,
Figure BDA0002629601130000072
the reconstructed signal is:
Figure BDA0002629601130000073
in the step, after wavelet transformation is carried out on original vibration signal data x (t), noise interference of vibration signals is reduced, and data are normalized.
And S3, classifying the reconstructed signal data according to the labels, and extracting corresponding time domain characteristics.
The fault data of the bearing is generally divided into three types, namely, fault position, damage size and load rotating speed, the label data set in the embodiment simultaneously comprises fault position and damage size information, the fault position information comprises inner ring fault, rolling body fault and outer ring fault, the damage size information comprises 0.178mm, 0.356mm and 0.533mm, the label in the embodiment comprises ten types of label data, wherein nine types of labels simultaneously comprise fault position information and damage size information, and the tenth type of label is a label of the bearing health state.
In this step, extracting the corresponding time domain features is to extract a mean value, a mean square value, a root mean square value, an average amplitude value, a kurtosis value, a peak-to-peak value, a standard deviation, a variance, a skewness value, a pulse factor, a skewness coefficient, a form factor, a kurtosis coefficient, a margin coefficient, a kurtosis factor, and a form entropy from the reconstructed signal data to form a total data set.
And S4, screening the classified signal data according to a set rule, and dividing the screened data into a training data set and a test data set.
In this embodiment, a Maximum Mean Difference (MMD) algorithm is used to screen the total data set to select a data set with a small difference in feature distribution. Given two distributions s and t, the MMD between them is defined as:
Figure BDA0002629601130000081
where E is the expectation for the allocation,
Figure BDA0002629601130000082
to map raw data to a function of the Regenerated Kernel Hilbert Space (RKHS), when s and t distributions are the same, MMD2(s, t) ═ 0, and the kernel function associated with this map is k (x)s,xt)＝<φ(xs),φ(xt)>；
Figure BDA0002629601130000083
And
Figure BDA0002629601130000084
the empirical estimate of MMD, representing distribution s and distribution t, respectively, is as follows:
Figure BDA0002629601130000085
when the data is consistent with 0 being less than or equal to LM(Ds,Dt) If the condition is less than 0.16, the data is selected as the screened data, and if the condition is not met, the data is removed.
And selecting 80% of the screened data as a training set, 20% of the screened data as a test set, setting 10% of the data in the training set with labels, and the rest 90% of the data in the training set as unlabeled data, and predicting and identifying the labels by using a semi-supervised deep belief network. In the embodiment, because the dependence on the label is greatly reduced by the semi-supervised deep belief network, training can be performed only by ensuring that 10% of data in the training set is labeled, and various costs for making label data are reduced.
And S5, inputting the training data set into the semi-supervised deep belief network, and carrying out deep training on the network.
In this embodiment, a semi-supervised deep belief network (SSDBN) is formed by stacking semi-supervised limited boltzmann machines (SSRBMs), where the SSDBN is composed of an input layer, a plurality of hidden layers, and an output layer, as shown in fig. 2, the SSDBN in this embodiment is configured by stacking 3 SSRBMs in total, and 4 layers in total, each layer has a plurality of neurons, and the neurons only have two states of activation and deactivation, in this embodiment, the number of neurons in the output layer is 10, the neurons in each layer are not connected to each other, the neurons in each layer are all connected to each other, and transmit information, update weight, and bias weight by using an activation function, the SSRBM is composed of a visible layer, a hidden layer, and a supervision layer, and an energy function of the SSRBM is defined as:
Figure BDA0002629601130000091
where v is a visible layer, h is a hidden layer, u is a supervisory layer, ψ ═ wij,pkj,ai,ck,bj) A is a visual layer bias value, b is a hidden layer bias value, c is a supervised layer bias value, λ is a weight parameter for controlling a ratio of supervised learning to unsupervised learning, w is a weight between the visual layer and the hidden layer, p is a weight between the supervised layers, and ψ is (w ═ isij,pkj,ai,ck,bj) Can be updated as:
Figure BDA0002629601130000092
wherein tau is iteration times, eta is learning rate;
the activation function connecting the SSRBM of each layer is generally a sigmoid function, and is modified into an Isigmoid function because the activation function is easy to cause the problems of gradient disappearance and gradient explosion:
Figure BDA0002629601130000093
wherein a is a threshold, α is a slope, and satisfies:
Figure BDA0002629601130000094
wherein alpha isminMinimum slope to work for the Isigmoid function.
In the embodiment, the Isigmoid activation function is used to reduce the probability of gradient explosion and gradient disappearance of the model, so that the model is more stable.
And S6, inputting the test data set into a semi-supervised deep belief network, and carrying out fault classification judgment on the data in the test data set through a deeply trained model.
In this embodiment, the semi-supervised deep belief network retains the trained weights and bias matrices, the supervision layer refers to the labeled data to assist in predicting the label of the unlabelled data, and the test data is transmitted from the input layer to the output layer according to the SSDBN after being input, and is finally classified.
In the embodiment, the judged bearing fault can be directly output by inputting the working data, the automation degree is high, the characteristics do not need to be manually selected, expert knowledge does not need to be relied on, and an end-to-end learning mode is completely realized; the method is more suitable for bearing fault diagnosis under multiple working conditions, has stronger compatibility and universality, and improves the bearing fault diagnosis precision; and the semi-supervised deep belief network needs a small amount of label data during model training, so that the training cost is reduced. In bearing fault diagnosis, effective data can be extracted from strong noise; automatic and intelligent automatic bearing fault diagnosis results can be realized without manually extracting features and depending on expert experience knowledge; the use of the labeled data is less, the cost for manufacturing the labeled data is reduced, and the precision of bearing fault diagnosis under the multi-working-condition is improved.
The principles and embodiments of the present application are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present application. The foregoing is only a preferred embodiment of the present application, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present application, and the technical features described above may be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention using its spirit and scope, as defined by the claims, may be directed to other uses and embodiments, or may be learned by practice of the invention.

Claims (8)

1. A bearing fault diagnosis method based on a semi-supervised deep belief network is characterized by comprising the following steps:
collecting vibration signal data;
performing wavelet transformation noise reduction on the vibration signal data and completing reconstruction;
classifying the reconstructed signal data according to the labels, and extracting corresponding time domain characteristics;
screening the classified signal data according to a set rule, and dividing the screened data into a training data set and a test data set;
inputting a training data set into a semi-supervised deep belief network, and carrying out deep training on the network;
inputting the test data set into a semi-supervised deep belief network, and carrying out fault classification judgment on the data in the test data set through a deeply trained model.
2. The bearing fault diagnosis method based on the semi-supervised deep belief network as claimed in claim 1, wherein the wavelet transform denoising and reconstructing are performed on the vibration signal data, specifically comprising:
considering the vibration signal data x (t) as a space V in a subspace of the function space0Space V0Can be decomposed into V1And W1I.e. V0＝V1+W1，V1Can be decomposed into V2And W2By analogy, Vj-1Can be decomposed into VjAnd WjJ is resolution, j is 0 or 1;
the signal decomposition and noise reduction process of the vibration signal data x (t) is as follows:
Figure FDA0002629601120000011
wherein
Figure FDA0002629601120000012
Is a linear combination weight with a resolution of 0, P0x (t) is x (t) at V0A smooth approximation of (i.e. a profile of x (t) at a resolution of 0, phi0k(t) is a scale function;
Figure FDA0002629601120000013
wherein
Figure FDA0002629601120000014
For linear combining weights at resolution 1, P1x (t) is x (t) at V1A smooth approximation of (i.e. a profile of x (t) at a resolution of 1, phi1k(t) is a scale function;
Figure FDA0002629601120000021
wherein
Figure FDA0002629601120000022
Discrete details at resolution 1, D1x (t) is x (t) in W1Projection of1k(t) is a wavelet function;
P0x(t)＝P1x(t)+D1x(t)；
Figure FDA0002629601120000023
Figure FDA0002629601120000024
the decomposed and denoised signals are respectively:
when j is equal to 0, the value of j,
Figure FDA0002629601120000025
when j is equal to 1, the value of j,
Figure FDA0002629601120000026
the reconstructed signal is:
Figure FDA0002629601120000027
3. the bearing fault diagnosis method based on the semi-supervised deep belief network of claim 1, wherein the label comprises fault location information and damage size information of the bearing, the fault location information comprises inner ring faults, rolling body faults and outer ring faults, and the damage size information comprises 0.178mm, 0.356mm and 0.533 mm; the labels comprise ten types, wherein nine types are labels simultaneously containing fault position information and damage size information, and the tenth type is labels of the bearing health state.
4. The bearing fault diagnosis method based on the semi-supervised deep belief network as claimed in claim 1, wherein the extracting of the corresponding time domain features specifically comprises: and extracting a mean value, a mean square value, a root mean square value, an average amplitude value, a kurtosis value, a peak-peak value, a standard deviation, a variance, a skewness value, a pulse factor, a skewness factor, a waveform factor, a kurtosis coefficient, a margin coefficient, a kurtosis factor and a waveform entropy from the reconstructed signal data to form a total data set.
5. The bearing fault diagnosis method based on the semi-supervised deep belief network as claimed in claim 4, wherein the step of screening the classified signal data according to a set rule specifically comprises the steps of:
the total data set was screened using the Maximum Mean Difference (MMD) algorithm, given two distributions s and t, the MMD between them is defined as:
Figure FDA0002629601120000031
where E is the expectation for the allocation,
Figure FDA0002629601120000032
to map raw data to a function of the Regenerated Kernel Hilbert Space (RKHS), when s and t distributions are the same, MMD2(s, t) ═ 0, and the kernel function associated with this map is k (x)s,xt)＝<φ(xs),φ(xt)>；
Figure FDA0002629601120000033
And
Figure FDA0002629601120000034
the empirical estimate of MMD, representing distribution s and distribution t, respectively, is as follows:
Figure FDA0002629601120000035
when the data is consistent with 0 being less than or equal to LM(Ds,Dt) If the condition is less than 0.16, the data is selected as the screened data, and if the condition is not met, the data is removed.
6. The method for diagnosing the bearing fault based on the semi-supervised deep belief network as recited in claim 5, wherein 80% of screened data are selected as a training set, 20% are selected as a test set, 10% of the data in the training set are provided with labels, and the remaining 90% of the data in the training set are unlabeled data, and the labels of the data are predicted and identified by the semi-supervised deep belief network.
7. The bearing fault diagnosis method based on the semi-supervised deep belief network as claimed in claim 1, wherein the inputting of the training data set into the semi-supervised deep belief network for deep training of the network specifically comprises:
the semi-supervised deep belief network (SSDBN) is formed by stacking semi-supervised restricted Boltzmann machines (SSRBMs), the SSDBN is composed of an input layer, a plurality of hidden layers and an output layer, each layer is provided with a plurality of neurons, the neurons only have two states of activation and non-activation, the neurons of each layer are not connected with each other, the neurons in each layer are fully connected, information is transmitted by using an activation function, weight values are updated, and weight values are weighted, the SSRBMs are composed of a visible layer, a hidden layer and a supervision layer, and the energy function of the SSRBMs is defined as:
Figure FDA0002629601120000041
where v is a visible layer, h is a hidden layer, u is a supervisory layer, ψ ═ wij,pkj,ai,ck,bj) A is a visual layer bias value, b is a hidden layer bias value, c is a supervised layer bias value, λ is a weight parameter for controlling a ratio of supervised learning to unsupervised learning, w is a weight between the visual layer and the hidden layer, p is a weight between the supervised layers, and ψ is (w ═ isij,pkj,ai,ck,bj) Can be updated as:
Figure FDA0002629601120000042
wherein tau is iteration times, eta is learning rate;
the activation function connecting the layers of the SSRBM is the Isigmoid function:
Figure FDA0002629601120000043
wherein a is a threshold, α is a slope, and satisfies:
Figure FDA0002629601120000044
wherein alpha isminMinimum slope to work for the Isigmoid function.
8. The bearing fault diagnosis method based on the semi-supervised deep belief network as claimed in claim 1, wherein the step of inputting the test data set into the semi-supervised deep belief network and performing fault classification and judgment on the data in the test data set through the model after deep training specifically comprises the steps of: the semi-supervised deep belief network reserves the trained weight and bias matrix, the supervision layer refers to the label data to assist in predicting the label of the unlabelled data, the test data are transmitted from the input layer to the output layer according to the SSDBN after being input, and finally the test data are classified.
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