CN109186973B - Mechanical fault diagnosis method of unsupervised deep learning network - Google Patents

Mechanical fault diagnosis method of unsupervised deep learning network Download PDF

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CN109186973B
CN109186973B CN201810949099.8A CN201810949099A CN109186973B CN 109186973 B CN109186973 B CN 109186973B CN 201810949099 A CN201810949099 A CN 201810949099A CN 109186973 B CN109186973 B CN 109186973B
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CN109186973A (en
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贾民平
赵孝礼
胡建中
许飞云
黄鹏
佘道明
鄢小安
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Southeast University
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    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G—PHYSICS
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Abstract

The invention discloses a mechanical fault diagnosis method of an unsupervised deep learning network, which comprises the following steps: (1) installing corresponding sensors near parts such as a bearing of mechanical equipment to acquire vibration signals to obtain mechanical vibration signals, (2) converting the acquired vibration signals into a mixed domain fault characteristic data set and dividing the mixed domain fault characteristic data set into a test and training sample characteristic subset; (3) inputting a training sample feature subset into a constructed Unsupervised Deep Learning Network (UDLN) model for learning and training, wherein the UDLN model consists of two layers of improved sparse filtering (L12SF) unsupervised feature extraction layers and one layer of weighted Euclidean distance similar affine (WE-AP) clustering layer; (4) and inputting the test sample into the trained diagnostic model to realize the whole-process unsupervised feature learning and fault clustering. (5) And calculating the recognition rate according to the membership degree of the test sample clustering division, thereby realizing fault recognition and diagnosis. The method is simple and easy to implement, and can carry out self-adaptive unsupervised fault diagnosis on various faults of mechanical equipment.

Description

Mechanical fault diagnosis method of unsupervised deep learning network
Technical Field
The invention relates to the technical field of fault diagnosis in industrial production, in particular to a mechanical fault diagnosis method of an unsupervised deep learning network.
Background
At present, industrial equipment is being developed in a large-scale, high-speed, and automated manner. Mechanical equipment is the most common component in the field of industrial equipment and is widely applied to important fields such as aviation, aerospace, transportation, intelligent manufacturing and the like. At present, the parts such as bearings are the most important power transmission and support parts of the machine, and according to statistics, 30% of mechanical faults are caused by local damage or defects of the parts such as the bearings, so that effective state monitoring and fault diagnosis are required to be carried out on mechanical equipment and key parts thereof.
Mechanical equipment such as cranes, rotating machinery and the like constantly generate a large amount of real-time information reflecting the operating conditions of the equipment, and the information comprises various physical quantities such as vibration, sound, temperature and the like. Once the mechanical equipment is abnormal, the corresponding physical information change is brought. In order to describe the operation condition of the mechanical equipment from different angles, more mechanical information needs to be collected, and effective intelligent monitoring and diagnosis are carried out on the mechanical equipment through data or information-based analysis. Therefore, the purpose of fault diagnosis is to realize effective diagnosis and analysis of equipment through the acquired mechanical information so as to reduce or reduce loss or harm caused by the fault of the mechanical equipment.
Because the equipment cluster for monitoring and diagnosing the mechanical equipment is large in scale, multiple in measuring points, high in sampling frequency and long in service life, the monitoring and diagnosing system acquires massive data, so that the field of mechanical health monitoring and management enters a big data era. However, in an actual industrial field, the phenomenon of "mechanical big data" also brings about a problem that the label information of the mechanical equipment is difficult to obtain. This is because the operating time of the mechanical equipment is much longer than the time when the fault occurs, sparsity of mechanical data is inevitable, and it is difficult to manually mark information. For this reason, the lack of tag information of the machine data and the sparsity of the data information present a series of challenges for fault diagnosis. Therefore, effective condition monitoring and fault identification of mechanical equipment in the limited or lack of training sample label information is a key issue which is always pending in the field of fault diagnosis.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a mechanical fault diagnosis method of an unsupervised deep learning network, aims to realize the whole-process unsupervised fault diagnosis of mechanical equipment from a characteristic extraction stage to a mode identification stage, and can provide a solvable effective scheme for the mechanical fault diagnosis under the condition of class label deficiency.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a mechanical fault diagnosis method of an unsupervised deep learning network, which comprises the following steps:
step 1, pre-selecting a tested part on mechanical equipment, and collecting a vibration signal of the tested part on the mechanical equipment;
step 2, converting the acquired vibration signals into a mixed domain fault feature data set, and dividing the mixed domain fault feature data set into a test sample feature subset and a training sample feature subset, wherein the test sample feature subset is used as a test sample, and the training sample feature subset is used as a training sample;
step 3, initializing parameters of an unsupervised deep learning network UDLN model, inputting training samples into the UDLN model for pre-training, and obtaining parameters of the UDLN model;
the pre-training is as follows:
the UDLN model consists of two-phase learning: firstly, inputting a training sample into an L12 norm sparse filtering L12SF feature extraction layer, wherein an L12 norm is a norm expression integrating a 1 norm and a 2 norm, and a feature competition special effect is generated by utilizing the L12SF so as to extract a feature value; then, the extracted characteristic value is sent to a weighted Euclidean distance similar affine WE-AP clustering layer to obtain parameters of the trained UDLN model;
step 4, inputting the test sample into the UDLN model after training to obtain a fault clustering and identifying result;
and 5, calculating various fault recognition rates according to the membership degree condition of the fault cluster and the cluster center, and realizing fault diagnosis of the mechanical equipment.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the tested parts in the step 1 comprise a crane, a bearing of a rotating machine and a gear.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the characteristics of the mixed domain fault characteristic data set in the step 2 comprise time domain, frequency domain and time-frequency domain characteristics.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the process of inputting training samples into the UDLN model for pre-training is as follows:
step 3.1, initializing parameters of the UDLN model;
step 3.2, taking the training sample feature subset as the original input of two layers of L12SF, and extracting the low-dimensional unsupervised features of the mixed domain fault feature data set layer by layer; wherein the objective function of L12SF is as follows:
Figure BDA0001771029090000021
wherein | | xi | purple1Representing a 1 norm, | × | | luminance2Representing 2 norm for M training samples
Figure BDA0001771029090000022
Denotes the i-th characteristic value, x, of the i-th training sampleiDenotes the ith training sample, wiIs the weight parameter of the ith training sample,
Figure BDA0001771029090000023
represents the minimization of wi,，Wl TIs the transpose of the weight parameter of the I characteristic value of the UDLN model, | | | count the luminanceL12L12 fused 1-norm to 2-norm; the L12 norm r (w) fused to the 1 norm and the 2 norm is:
Figure BDA0001771029090000031
wherein the content of the first and second substances,
Figure BDA0001771029090000032
for regularizing norm adjustment coefficient, extracting l characteristic values f of new ith training sample by optimizing objective functionl iIs composed of
fl i＝G(Wl Txi)
Wherein G () is an activation function;
will f isl i＝G(Wl Txi) Optimizing the objective function of L12SF by adopting an L-BFGS algorithm until convergence;
step 3.3, inputting the characteristic values extracted in the step 3.2 into a clustering layer of the WE-AP for fault clustering learning and fault division, wherein the WE-AP clustering firstly initializes a weighting similarity matrix S with N training samplesw
Figure BDA0001771029090000033
Wherein x is1kAnd x2kRepresenting the kth feature, S, of two different training sampleskRepresenting the variance of two training samples, N being the number of eigenvalues, N ═ M;
meanwhile, the credibility and the availability of the ith training sample are calculated in the specific calculation mode
R(i,k)＝S(i,k)-max{A(i,j)+S(i,j)}
St.j＝1,2,...,N and j≠i,k
Figure BDA0001771029090000034
St.j＝1,2,...,N and j≠i,k
R(k,k)＝B(k)-max{A(k,j)+S(k,j)}
St.j＝1,2,...,N and j≠k
Wherein, b (k) is a prior value representing the tendency of each training sample to be selected as a cluster center point; s (i, K) represents a weighted similarity matrix of the ith training sample and the kth training sample, A (i, j) represents the availability of the ith training sample for selecting the jth training sample as the clustering center of the ith training sample, S (i, j) represents the weighted similarity matrix of the ith training sample and the jth training sample, A (i, K) represents the availability of the ith training sample for selecting the kth training sample as the clustering center of the ith training sample, A (i, j) represents the availability of the ith training sample for selecting the jth training sample as the clustering center of the ith training sample, R (K, K) represents the reliability of the kth training sample for selecting the kth training sample as the clustering center of the ith training sample, R (i, K) represents the reliability of the ith training sample for selecting the kth training sample as the clustering center of the ith training sample, and R (K, K) represents the reliability of the kth training sample for selecting the kth training sample as the clustering center of the kth training sample, a (k, j) represents the availability of the kth training sample to select the jth training sample as the clustering center of the kth training sample, and S (k, j) represents the weighted similarity matrix of the kth training sample and the jth training sample.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, in step 3.2, G (×) is an activation function using soft absolute values:
Figure BDA0001771029090000035
where σ is the activation threshold, then
Figure BDA0001771029090000041
The L-BFGS algorithm is used to optimize the objective function of L12SF until convergence.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the sigma is 10-8。
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the condition and membership degree condition of the kth training sample as a clustering center are determined according to the following formula calculated by the UDLN model in step 5
R(k,k)+A(k,k)＞0
Wherein, R (k, k) represents the credibility of the k training sample selecting the k training sample as the clustering center of the k training sample, and A (k, k) represents the availability of the k training sample selecting the k training sample as the clustering center of the k training sample;
according to a preset maximum iteration number tmaxUpdating the reliability and availability t of each training samplemaxThen, the updating mode is
R(i,k)＝(1-lam)*R(i,k)+lam*R(i-1,k)
A(i,k)＝(1-lam)*A(i,k)+lam*A(i-1,k)
Wherein lam is a damping factor, wherein R (i-1, k) represents the reliability of selecting the kth training sample as the clustering center of the (i-1) th training sample, and A (i-1, k) represents the availability of selecting the kth training sample as the clustering center of the (i-1) th training sample;
and finally, calculating various fault recognition rates according to the membership degree condition of the fault cluster and the cluster center, and realizing fault diagnosis of the mechanical equipment.
As a further optimization scheme of the mechanical fault diagnosis method of the unsupervised deep learning network, the model parameters of the UDLN in the step 3 comprise the weight parameter w of the ith training sampleiAnd regularization norm adjustment coefficient
Figure BDA0001771029090000042
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the improved L12 norm sparse filtering (L12SF) unsupervised feature extraction network can extract unsupervised special diagnosis information of fault data layer by layer, and the newly defined L12 norm enhances the generalization capability of extracted low-dimensional features;
(2) the improved weighted Euclidean similar affine (WE-AP) clustering can more accurately realize the self-adaptive unsupervised clustering of fault categories, overcomes the defects that the traditional clustering method needs to preset the clustering number and the clustering center, and simultaneously highlights the contribution degree of different sample characteristics to clustering;
(3) on the basis of L12SF and WE-AP clustering, a brand-new unsupervised deep learning network-UDLN is constructed, and the neural network can realize whole-course unsupervised learning from feature extraction to pattern recognition;
(4) the invention discloses a novel mechanical fault diagnosis method based on a UDLN model, which realizes unsupervised intelligent mechanical fault diagnosis under the condition of no class label information and verifies the feasibility of the method by vibration signals of mechanical equipment. In addition, the method is simple and easy to implement, and is suitable for online or field fault diagnosis of mechanical equipment under the condition that the class labels are lacked.
Drawings
FIG. 1 is a flow chart of the present technique.
Fig. 2 is a schematic structural diagram of a multilayer L12SF network.
FIG. 3 is a schematic diagram of the UDLN model architecture.
FIG. 4 is a time domain and frequency domain waveform envelope of various types of fault signals of a bearing.
Fig. 5 is a confusion matrix for mechanical fault identification based on UDLN fault diagnosis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
In actual industrial field diagnosis, multilayer unsupervised learning can provide one of solvable schemes for training samples and class labels thereof in the absence situation, and therefore the invention aims to construct a new Unsupervised Deep Learning Network (UDLN) model through the proposed L12 norm sparse filtering (L12SF) and weighted Euclidean distance similar affine (WE-AP) in a combined mode, and a mechanical fault diagnosis method based on the UDLN model is invented on the basis.
Fig. 1 shows a flow of a method for diagnosing a mechanical fault in an unsupervised deep learning network, which may be summarized as follows:
(1) firstly, arranging corresponding sensors at key positions of mechanical equipment (such as a crane and the like) to pick up vibration signals of the mechanical equipment;
(2) converting the vibration signal into a multi-domain mixed fault feature data set, and dividing the multi-domain mixed fault feature data set into a test sample feature subset and a training sample feature subset;
currently, the most common features include time domain, frequency domain, time-frequency domain, etc., which well characterize the health of a mechanical failure data set. A mixed multi-domain feature set shown in Table 1 was constructed from the bearing vibration signal samples and recorded as feature data set H.
TABLE 1 statistical characteristic parameters of time domain, frequency spectrum, time-frequency domain
Figure BDA0001771029090000061
(3) Initializing network parameters, and inputting the training sample feature subset into a UDLN model for training and learning;
the specific method is described as follows:
(3.1) description of L12SF method
Compared with other feature learning methods, the sparse filtering does not need to try to construct a model for data distribution, and only needs to optimize the sparsity of a simple loss function 2-norm standardization feature. Given a sample set with M training samples as follows
fl i＝Wl Txi
Wherein f isl iI-th characteristic value, W, representing the i-th sample (column)l TThe transpose of the weight matrix representing the l-th eigenvalue.
Sparse filtering uses a 2-norm to normalize the sparsity of the data. These features form a feature matrix. We first normalized each row of the feature matrix in all samples with a 2-norm.
Figure BDA0001771029090000062
Wherein the content of the first and second substances,
Figure BDA0001771029090000063
denotes the l characteristic value, f, of the 2 norm normalized training samplelRepresenting the ith eigenvalue of the training sample. Each column is then normalized by its 2 norm
Figure BDA0001771029090000064
Wherein the content of the first and second substances,
Figure BDA0001771029090000065
the ith sample representing 2-norm normalization,
Figure BDA0001771029090000066
the ith sample representing the 2-norm normalized eigenvalue. Finally, the weight matrix WT lThis can be solved by constraining the 1 norm by optimizing the cost function for each sample, as shown below. The normalized features are optimized for sparsity by using a 1 penalty. Therefore, for a data with M samples, the objective function for a given sparse filter is:
Figure BDA0001771029090000067
wherein M represents the number of training samples, | Y calculation1Representing a 1 norm, | × | | luminance2Representing a 2 norm. Meanwhile, the L1 norm can make data more sparse, the 2 norm can prevent overfitting of the data, and the generalization capability of the model is improved. Therefore, if the advantages of the 1 norm and the 2 norm are fused, the sparse characteristic of the data and the generalization performance of the neural network can be fully adjusted. Therefore, the present invention redefines a norm R (w):
Figure BDA0001771029090000071
wherein the content of the first and second substances,
Figure BDA0001771029090000072
adjusting the coefficient for regularization norm, wiIs the weight parameter of the ith training sample of the model. Finally, the objective function of the above formula is changed to:
Figure BDA0001771029090000073
wherein | | xi | purpleL12The L12 norm is expressed, and the learning characteristic can find more nonlinear information from the input sample through the objective function in the optimization formula, so that the generalization capability is better.
fl i＝G(Wl Txi)
Where G (×) represents the activation function for soft absolute values. Therefore, the structural schematic diagram of L12SF is shown in fig. 2.
(3.2) description of WE-AP clustering method
The specific calculation steps of WE-AP clustering are shown below
(1): calculating a weighted similarity matrix SwDefining the maximum iteration tmax＝1000.
Figure BDA0001771029090000074
(2): calculating the credibility R and the availability A of each sample point in a specific way
R(i,k)＝S(i,k)-max{A(i,j)+S(i,j)}
St.j＝1,2,...,N and j≠i,k
Figure BDA0001771029090000075
St.j＝1,2,...,N and j≠i,k
R(k,k)＝B(k)-max{A(k,j)+S(k,j)}
St.j＝1,2,...,N and j≠k
Wherein, b (k) is a prior value representing the tendency of each training sample to be selected as a cluster center point; s (k, j) represents a weighted similarity matrix of the training samples, R (i, k) represents a degree to which the training sample i is suitable as a clustering center of the training sample k, and a (k, j) represents a degree to which the training sample k selects the training sample j as its clustering center.
(3): and (3) determining whether the kth training sample can be used as a clustering central point according to the calculation credibility, wherein the similarity value of the training sample is set as B (k):
R(k,k)+A(k,k)＞0
where R (k, k) represents the degree to which the training sample k is suitable as the clustering center of the training sample k, and a (k, k) represents the degree to which the training sample k selects the training sample k as its clustering center.
(4): and updating the credibility R and the availability A. The updating method is
R(i,k)＝(1-lam)*R(i,k)+lam*R(i-1,k)
A(i,k)＝(1-lam)*A(i,k)+lam*A(i-1,k)
Where lam is a damping factor, R (i-1, k) represents the degree to which the training sample i-1 is suitable as the clustering center of the training sample k, and A (i-1, k) represents the degree to which the training sample i-1 selects the training sample k as the clustering center. The purpose of the method is to balance the credibility R and the availability A in two iterations before and after the oscillation is avoided.
(5): checking whether a termination condition is met, i.e. the number of iterations reaches a maximum iteration tmaxAnd if not, jumping to the step (2) in 3.2.
(3.3) description of the UDLN method
UDLN consists of two phases of learning: firstly, inputting a sample into a corrected L12 sparse filter (L12SF), and extracting deep characteristic expression by using the algorithm; then, the deep features are sent to a WE-AP neural network, an internal distribution model of input data is further established through nonlinear function mapping, and a network weight of the whole learned UDLN model is obtained. During recognition, firstly, sparse filtering is carried out on a test sample, and then the extracted features are input into a trained WE-AP-based UDLN directly having a classification effect. Compared with deep learning and other fault diagnosis methods, the whole model does not need to use a class label, and whole-process unsupervised feature extraction and fault mode identification can be realized. The training process for the UDLN model is as follows: the algorithm pre-training stage consists of two layers of feature extraction layers consisting of an L12SF structure and one layer of unsupervised WE-AP clustering. The structure of the UDLN model is shown in FIG. 3.
The specific implementation mode is as follows:
example 1: in this section, to verify the performance of the proposed UDLN-based mechanical fault diagnosis model, it is necessary to simulate various rolling bearing faults. The experiment is carried out on an accelerated bearing life tester (ABLT-1A is provided by Hangzhou bearing test research center), and the main components of the experiment table are as follows: the test bench comprises a computer control system, a test headstock, an experimental head, a lubricating system, a transmission system, a loading system and a test and data acquisition system. The tester is designed with four bearings mounted on a shaft driven by an ac motor, with its drive system supported by a rubber belt for connecting the ac motor to the shaft using two pulleys. Meanwhile, various faults of the 6205 single-row deep groove ball bearing are simulated in an experiment. The experimental linear cutting respectively simulates five faults of 6205 rolling bearing, namely inner ring fault, inner ring and outer ring composite weak fault, rolling element outer ring composite weak fault and the like. And acquiring vibration data of a group of 20480 points every 5 minutes by using an NI9234 data acquisition card and a PCB acceleration sensor. The sampling frequency is 10240Hz, and the rotating speed is 1050 r/min. Each data acquisition system comprises four acceleration sensors and a data acquisition card. 6205 the test conditions and experimental data of the bearing are shown in table 2.
TABLE 26205 bearing test conditions
Figure BDA0001771029090000081
Figure BDA0001771029090000091
In the bearing fault data, there are weak faults and also serious faults in various bearing states, and also various fault conditions under different conditions such as composite faults, and the like, and the time domain and single-side spectrum frequency domain oscillograms of vibration signals are shown in fig. 4. It can be seen that, compared with a serious fault, a weak fault vibration signal is weak relatively and is greatly influenced by the noise interference degree, the impact characteristic is not obvious, the resonance frequency band in a frequency spectrum is not obvious, and the early weak fault is difficult to observe. The traditional time-frequency method is difficult to quantify the fault degree and the fault category, and needs to rely on a large amount of expert knowledge and field experience, so that the actual fault diagnosis is difficult. Therefore, an intelligent fault diagnosis method is required to quantify the fault diagnosis result, and the intelligent fault diagnosis method based on machine learning is widely applied. In order to display more diagnostic information, fig. 5 shows the processing result of the proposed fault diagnosis model on the bearing fault data feature set H according to the fault diagnosis flowchart 1, and the test sample confusion matrix of the fault diagnosis result is shown in fig. 5. As can be seen from fig. 5, the proposed method classifies the test samples of several second-class samples into the fourth class by mistake, because the discrimination between two classes of composite faults is not obvious, and all the faults including the inner ring are easy to be confused, and the clustering of other classes is successful.
To verify the proposed UDLN-based feature extraction and pattern recognition capabilities. The invention relates to a method for preparing L12SF + AP, L12SF + Kmeans; l12SF + FCM was used as a comparative test for the UDLN diagnostic model, and is referred to as { UDLN — FD 1; l12SF + AP FD 2; l12SF + Kmeans FD 3; l12SF + FCM FD4 }. According to the above fault diagnosis flowchart 1, using the other diagnosis models as a comparison test, respectively inputting the time domain, frequency domain and mixed domain characteristics into the 4 types of fault diagnosis models, and classifying according to membership and division to obtain corresponding recognition results, as shown in table 3:
TABLE 3 clustering results based on different feature data sets
Figure BDA0001771029090000092
In summary, to make intelligent fault diagnosis independent of a priori knowledge and the expertise of the diagnostician. The invention provides a mechanical fault diagnosis method based on an unsupervised learning neural network (ULDN). The ULDN model proposed by the method is composed of two layers of improved L12SF and one layer of improved AP clustering. In the ULDN approach, improved sparse filtering can adaptively capture features with discriminatory information from a vibration signal in an unsupervised manner; these features are then input to the improved AP cluster and the health is classified in an unsupervised manner. Example research of a bearing data set shows that the method not only can learn high-level features with discriminability, but also can effectively realize unsupervised full-automatic fault diagnosis. Under the condition of not using label information, the method can fully utilize the advantages of unsupervised learning, improve the accuracy of mechanical fault diagnosis and automatically identify fault health conditions.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (7)

1. A mechanical fault diagnosis method of an unsupervised deep learning network is characterized by comprising the following steps:
step 1, pre-selecting a tested part on mechanical equipment, and collecting a vibration signal of the tested part on the mechanical equipment;
step 2, converting the acquired vibration signals into a mixed domain fault feature data set, and dividing the mixed domain fault feature data set into a test sample feature subset and a training sample feature subset, wherein the test sample feature subset is used as a test sample, and the training sample feature subset is used as a training sample;
step 3, initializing parameters of an unsupervised deep learning network UDLN model, inputting training samples into the UDLN model for pre-training, and obtaining parameters of the UDLN model;
the pre-training is as follows:
the UDLN model consists of two-phase learning: firstly, inputting a training sample into an L12 norm sparse filtering L12SF feature extraction layer, wherein an L12 norm is a norm expression integrating a 1 norm and a 2 norm, and a feature competition special effect is generated by utilizing the L12SF so as to extract a feature value; then, the extracted characteristic value is sent to a weighted Euclidean distance similar affine WE-AP clustering layer to obtain parameters of the trained UDLN model;
the process of inputting training samples into the UDLN model for pre-training is as follows:
step 3.1, initializing parameters of the UDLN model;
step 3.2, taking the training sample feature subset as the original input of two layers of L12SF, and extracting the low-dimensional unsupervised features of the mixed domain fault feature data set layer by layer; wherein the objective function of L12SF is as follows:
Figure FDA0002339969620000011
wherein | | xi | purple1Representing a 1 norm, | × | | luminance2Representing 2 norm for M training samples
Figure FDA0002339969620000012
Figure FDA0002339969620000013
Denotes the i-th characteristic value, x, of the i-th training sampleiDenotes the ith training sample, wiIs the weight parameter of the ith training sample,
Figure FDA0002339969620000014
represents the minimization of wi,，Wl TIs the transpose of the weight parameter of the I characteristic value of the UDLN model, | | | count the luminanceL12L12 fused 1-norm to 2-norm; the L12 norm r (w) fused to the 1 norm and the 2 norm is:
Figure FDA0002339969620000015
wherein the content of the first and second substances,
Figure FDA0002339969620000016
for regularizing norm adjustment coefficient, extracting l characteristic values f of new ith training sample by optimizing objective functionl iIs composed of
fl i＝G(Wl Txi)
Wherein G () is an activation function;
will f isl i＝G(Wl Txi) Optimizing the objective function of L12SF by adopting an L-BFGS algorithm until convergence;
step 3.3, inputting the characteristic values extracted in the step 3.2 into a clustering layer of the WE-AP for fault clustering learning and fault division, wherein the WE-AP clustering firstly initializes a weighting similarity matrix S with N training samplesw
Figure FDA0002339969620000021
Wherein x is1kAnd x2kRepresenting the kth feature, S, of two different training sampleskRepresenting the variance of two training samples, N being the number of eigenvalues, N ═ M;
meanwhile, the credibility and the availability of the ith training sample are calculated in the specific calculation mode
R(i,k)＝S(i,k)-max{A(i,j)+S(i,j)}
St.j＝1,2,...,Nandj≠i,k
Figure FDA0002339969620000022
St.j＝1,2,...,Nandj≠i,k
R(k,k)＝B(k)-max{A(k,j)+S(k,j)}
St.j＝1,2,...,Nandj≠k
Wherein, b (k) is a prior value representing the tendency of each training sample to be selected as a cluster center point; s (i, K) represents a weighted similarity matrix of the ith training sample and the kth training sample, A (i, j) represents the availability of the ith training sample for selecting the jth training sample as the clustering center of the ith training sample, S (i, j) represents the weighted similarity matrix of the ith training sample and the jth training sample, A (i, K) represents the availability of the ith training sample for selecting the kth training sample as the clustering center of the ith training sample, A (i, j) represents the availability of the ith training sample for selecting the jth training sample as the clustering center of the ith training sample, R (K, K) represents the reliability of the kth training sample for selecting the kth training sample as the clustering center of the ith training sample, R (i, K) represents the reliability of the ith training sample for selecting the kth training sample as the clustering center of the ith training sample, and R (K, K) represents the reliability of the kth training sample for selecting the kth training sample as the clustering center of the kth training sample, a (k, j) represents the availability of the kth training sample for selecting the jth training sample as a clustering center of the kth training sample, and S (k, j) represents a weighted similarity matrix of the kth training sample and the jth training sample;
step 4, inputting the test sample into the UDLN model after training to obtain a fault clustering and identifying result;
and 5, calculating various fault recognition rates according to the membership degree condition of the fault cluster and the cluster center, and realizing fault diagnosis of the mechanical equipment.
2. The method for diagnosing the mechanical fault of the unsupervised deep learning network according to claim 1, wherein the tested parts in the step 1 comprise a crane, a bearing of a rotating machine and a gear.
3. The method as claimed in claim 1, wherein the characteristics of the mixed-domain fault feature data set in step 2 include time-domain, frequency-domain and time-frequency-domain characteristics.
4. A method for diagnosing mechanical failure in an unsupervised deep learning network according to claim 1, wherein in step 3.2, G (×) is an activation function using soft absolute values:
Figure FDA0002339969620000031
where σ is the activation threshold, then
Figure FDA0002339969620000032
The L-BFGS algorithm is used to optimize the objective function of L12SF until convergence.
5. The method of claim 4, wherein σ is 10 ═ 10 ] for diagnosing mechanical failure in the unsupervised deep learning network-8。
6. The method as claimed in claim 1, wherein in step 5, the following formula is calculated according to the UDLN model to determine the condition and membership degree of the kth training sample as the clustering center
R(k,k)+A(k,k)＞0
Wherein, R (k, k) represents the credibility of the k training sample selecting the k training sample as the clustering center of the k training sample, and A (k, k) represents the availability of the k training sample selecting the k training sample as the clustering center of the k training sample;
according to a preset maximum iteration number tmaxUpdating the reliability and availability t of each training samplemaxThen, the updating mode is
R(i,k)＝(1-lam)*R(i,k)+lam*R(i-1,k)
A(i,k)＝(1-lam)*A(i,k)+lam*A(i-1,k)
Wherein lam is a damping factor, wherein R (i-1, k) represents the reliability of selecting the kth training sample as the clustering center of the (i-1) th training sample, and A (i-1, k) represents the availability of selecting the kth training sample as the clustering center of the (i-1) th training sample;
and finally, calculating various fault recognition rates according to the membership degree condition of the fault cluster and the cluster center, and realizing fault diagnosis of the mechanical equipment.
7. The method as claimed in claim 1, wherein the model parameters of UDLN in step 3 include weight parameter w of ith training sampleiAnd regularization norm adjustment coefficient
Figure FDA0002339969620000033
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