CN108398268B - Bearing performance degradation evaluation method - Google Patents

Bearing performance degradation evaluation method Download PDF

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CN108398268B
CN108398268B CN201810214288.0A CN201810214288A CN108398268B CN 108398268 B CN108398268 B CN 108398268B CN 201810214288 A CN201810214288 A CN 201810214288A CN 108398268 B CN108398268 B CN 108398268B
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赵光权
彭喜元
刘小勇
刘月峰
姜泽东
刘莉
高奇
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Harbin Institute of Technology
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Abstract

A bearing performance degradation evaluation method based on a stacking denoising autoencoder and self-organizing mapping is used for evaluating the technical field of bearing degradation. The method solves the problems that a large amount of expert experience and supervised training are required for extracting degradation characteristics and manual participation is required for label selection in the traditional HI curve construction. The method comprises the steps that a stack denoising autoencoder is built by 6 denoising autoencoder mechanisms to carry out multi-layer feature extraction on original vibration signal data, parameters are finely adjusted by using a BP algorithm after network pre-training is carried out on training set data, output 100-dimensional features are input into an SOM network to be trained to obtain HI corresponding to each time point, and an HI curve of a training set is built; inputting test set data into a trained stacked denoising autoencoder and an SOM network to obtain HI at each time point, and constructing an HI curve; and respectively smoothing the HI curves of the training set and the testing set to obtain the smoothed HI curves. The method can be applied to the field of evaluating the performance degradation of the bearing.

Description

Bearing performance degradation evaluation method
Technical Field
The invention belongs to the technical field of bearing residual life prediction, and particularly relates to a bearing performance degradation evaluation method based on a stacking denoising self-encoder and self-organizing mapping.
Background
The bearing is one of the most common and vulnerable mechanical elements in the industrial field, and the guarantee of the reliable operation state of the bearing has very important practical significance for improving the system safety and reducing the equipment maintenance cost. The bearing health factor (HI) is used as a characteristic quantity for evaluating the bearing health level and is an index [1] for representing the degradation state or degradation degree of the bearing health level, so that the establishment of a good HI curve has important significance for subsequent prediction of the residual life of the bearing.
However, as the complexity of bearing operating conditions increases, establishing accurate physical analytical models for the bearing degradation process becomes increasingly complex. The data-driven-based method benefits from the development of sensor technology and storage technology, and can obtain a large amount of bearing health condition monitoring data, so that the method is gradually the mainstream way for researching the residual life prediction of the bearing. The adopted data driving method for obtaining the health factor representing the degradation behavior of the bearing can be divided into direct prediction and indirect prediction, and the direct prediction method directly uses original data as the health factor of a measured object, so that the requirement of better trend is difficult to meet, and the monotonicity of a health factor curve is crucial to the subsequent prediction of the residual life of the bearing, so that domestic and foreign scholars make extensive research on the method for indirectly constructing the HI curve.
In the process of indirectly constructing the HI curve, because the original data cannot be directly used as a health factor, an effective feature extraction process is required to obtain a feature set, so that the original data is subjected to more advanced characterization, redundant features are removed by feature selection on the basis, then feature fusion is required to be continuously performed under necessary conditions, and the health state of the bearing is reflected by combining various features. As a key step, feature extraction methods mainly include a method based on a conventional signal processing technique and a machine learning method. Zhang x. et al, using wavelet analysis to perform feature extraction on the vibration signal of the bearing, obtain an HI curve with better trend; shenzhongjie et al propose a relative root mean square value which is not affected by individual differences of bearings to represent the health status of bearings, and have a good rising trend [3 ]. In the bearing HI construction method based on machine learning, methods such as an artificial neural network and a related vector machine are widely applied, Liang Guo et al firstly extract a plurality of features based on similarity and time-frequency domain features, and then input the features into a recurrent neural network to construct an HI curve; maio f.d. et al obtain the correlation vector using a correlation vector machine, and then fit the degradation condition of the bearing using an exponential function. Although the traditional data-driven approach has achieved significant success in bearing HI-curve construction, the following problems remain: the degradation feature extraction still depends on a large amount of expert experience and a traditional signal processing method; part of HI construction model training usually adopts a supervision mode, namely, a real output value corresponding to input needs to be provided as a label in the training process, and the label selection needs to depend on manual participation, so that time is consumed and no consistent standard exists; in order to obtain a comprehensive monotonic HI curve, multiple signal processing methods are often adopted for fusion and parameters are selected depending on manual experience aiming at specific prediction problems, and certain universality is lacked.
Disclosure of Invention
The invention aims to solve the problem that the traditional data driving method still depends on a great deal of expert experience for extracting degradation characteristics in the construction of a bearing HI curve; the method comprises the following steps that (1) a supervision mode is usually adopted for training part of HI construction models, real output values corresponding to input are required to be provided as labels in the training process, and the labels are selected by manual participation, so that time is consumed, and no consistent standard exists; aiming at the specific prediction problem, a plurality of signal processing methods are fused, parameters are selected by depending on manual experience, and the problem of lack of certain universality is solved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a bearing performance degradation evaluation method based on a stacking denoising autoencoder and self-organizing mapping comprises the following specific steps:
acquiring original vibration signal data in the whole life cycle of a bearing by using a vibration sensor, and taking the original vibration signal data as input data x of a stacking denoising self-encoder;
step two, carrying out absolute value taking operation on the input data x in the step one, and normalizing the input data x to be in a [0,1] interval; using a part of vibration data in input data x as training set data, and using other part of vibration data in the input data x as test set data;
establishing a stacked denoising self-encoder network consisting of 6 denoising automatic encoders for performing feature extraction on training set data and test set data, wherein the first denoising self-encoder, the second denoising self-encoder and the third denoising self-encoder form an encoding network of the stacked denoising self-encoder network, and the fourth denoising self-encoder, the fifth denoising self-encoder and the sixth denoising self-encoder form a decoding network of the stacked denoising self-encoder network;
inputting the training set data determined in the second step into a coding network of the stacking denoising self-coder network, and enabling the training set data to pass through the unsupervised pre-training of a first denoising self-coder, a second denoising self-coder and a third denoising self-coder of the coding network in sequence to obtain a coding parameter theta 1 ═ W of the first denoising self-coder of the coding network1,b1And coding parameters theta 2 ═ W of a second denoising self-coding machine2,b2W and the encoding parameter θ 3 ═ W of the third denoise self-encoder3,b3}；
Setting the encoding weight W of the fourth denoising self-encoding machine of the decoding network4Is W3Transpose of (2), the coding weight W of the fifth denoising autocoder5Is W2Transpose of (3), coding weight W of sixth denoising self-coder6Is W1Transposing; after the pre-training is finished, fine tuning is carried out on the network parameters of the stacking denoising self-encoder network by using a BP algorithm; obtaining 100-dimensional features output by the coding network by using the network parameters after fine tuning as 100-dimensional features extracted at each time point of training set data;
step four, taking the 100-dimensional characteristics of each time point of the training set data extracted in the step three as the input of the self-organizing mapping network, wherein the number of nodes of the input layer of the SOM network is consistent with the number of the characteristics; after iterative training, calculating a bearing health factor of each time point of training set data, and further constructing a bearing health factor curve on the training set;
inputting test set data into a trained stacked self-encoder network by using the method in the third step, extracting features through a plurality of hidden layers, inputting the extracted features of the test set into the trained self-organizing map network by using the method in the fourth step, calculating to obtain a bearing health factor corresponding to each time point on the test set, and constructing a bearing health factor curve on the test set;
and step six, smoothing the bearing health factor curves constructed in the step four and the step five respectively to obtain smoothed bearing health factor curves, and evaluating the performance degradation condition of the bearing by using the smoothed bearing health factor curves.
The invention has the beneficial effects that: the invention provides a bearing performance degradation evaluation method based on a stack denoising self-encoder and self-organizing mapping, which utilizes 6 denoising self-encoding mechanisms to build a stack denoising self-encoder to directly carry out multi-layer characteristic extraction on original vibration signal data of a bearing, training set data is trained in an unsupervised mode, a hidden layer of each denoising self-encoder is reserved after the training is finished, the hidden layer is used as the input of the next denoising self-encoder, the analogy is carried out, the parameters are finely adjusted by the BP algorithm on the whole network, finally, 100-dimensional characteristic output of an encoding network is obtained, the 100-dimensional characteristics extracted by the stack denoising self-encoder are input into an SOM network for training, and the corresponding bearing health factor at each time point on the training set is calculated after the training is finished, further constructing a bearing health factor curve on the training set; inputting the data of the test set into a trained stacked denoising self-encoder and an SOM network to obtain a bearing health factor corresponding to each time point on the test set, and constructing a bearing health factor curve on the test set; and smoothing the bearing health factor curves of the training set and the testing set respectively to finally obtain a smoothed bearing health factor curve, and evaluating the performance degradation condition of the bearing by using the smoothed bearing health factor curve.
Compared with the traditional method, the bearing health factor curve constructed by the method can better depict the degradation trend of the health condition in the whole life cycle of the bearing, the local oscillation is smaller, the curve is smoother, the time relevance of the bearing health factor curve on the tested bearing data is improved by about 10%, the monotonicity is improved by about 2.5%, and therefore the degradation condition of the bearing performance can be better estimated.
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FIG. 1 is a flow chart of a bearing performance degradation evaluation method based on a stacked denoising autoencoder and self-organizing mapping according to the present invention;
FIG. 2 is a block diagram of a stacked denoising autoencoder according to the present invention;
FIG. 3 is a schematic diagram of a first denoising self-coding machine according to the present invention;
h is a hidden layer;
FIG. 4 is a graph illustrating the raw vibration signals of the test set data Bearing1_3 according to the present invention;
FIG. 5 is a schematic diagram of a smoothed bearing health factor curve in accordance with the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The first embodiment is as follows: the present embodiment will be described with reference to fig. 1 and 2. The embodiment of the invention discloses a bearing performance degradation evaluation method based on a stack denoising self-encoder and self-organizing mapping, which comprises the following specific steps:
acquiring original vibration signal data in the whole life cycle of a bearing by using a vibration sensor, and taking the original vibration signal data as input data x of a stacking denoising self-encoder;
step two, carrying out absolute value taking operation on the input data x in the step one, and normalizing the input data x to be in a [0,1] interval; using a part of vibration data in input data x as training set data, and using other part of vibration data in the input data x as test set data;
establishing a stacked denoising self-encoder network consisting of 6 denoising automatic encoders for performing feature extraction on training set data and test set data, wherein the first denoising self-encoder, the second denoising self-encoder and the third denoising self-encoder form an encoding network of the stacked denoising self-encoder network, and the fourth denoising self-encoder, the fifth denoising self-encoder and the sixth denoising self-encoder form a decoding network of the stacked denoising self-encoder network;
inputting the training set data determined in the second step into a coding network of the stacking denoising self-coder network, and enabling the training set data to pass through the unsupervised pre-training of a first denoising self-coder, a second denoising self-coder and a third denoising self-coder of the coding network in sequence to obtain a coding parameter theta 1 ═ W of the first denoising self-coder of the coding network1,b1And coding parameters theta 2 ═ W of a second denoising self-coding machine2,b2W and the encoding parameter θ 3 ═ W of the third denoise self-encoder3,b3}；
Setting the encoding weight W of the fourth denoising self-encoding machine of the decoding network4Is W3Transpose of (2), the coding weight W of the fifth denoising autocoder5Is W2Transpose of (3), coding weight W of sixth denoising self-coder6Is W1Transposing; after the pre-training is finished, fine tuning is carried out on the network parameters of the stacking denoising self-encoder network by using a BP algorithm; obtaining 100-dimensional features output by the coding network by using the network parameters after fine tuning as 100-dimensional features extracted at each time point of training set data;
step four, taking the 100-dimensional characteristics of each time point of the training set data extracted in the step three as the input of a self-organizing map (SOM) network, wherein the number of nodes of an input layer of the SOM network is consistent with the number of characteristics; after iterative training, calculating a bearing health factor of each time point of training set data, and further constructing a bearing health factor curve on the training set;
inputting test set data into a trained stacked self-encoder network by using the method in the third step, extracting features through a plurality of hidden layers, inputting the extracted features of the test set into the trained self-organizing map network by using the method in the fourth step, calculating to obtain a bearing health factor corresponding to each time point on the test set, and constructing a bearing health factor curve on the test set;
and step six, smoothing the bearing health factor curves constructed in the step four and the step five respectively to obtain smoothed bearing health factor curves, and evaluating the performance degradation condition of the bearing by using the smoothed bearing health factor curves.
The stack denoising autoencoder network established in the embodiment can be composed of an even number of denoising autoencoders of 2, 4, 6, 8 or more than 8, and can be determined according to the actual condition of data; moreover, 100-dimensional features at each time point are extracted, and when the 100-dimensional features are extracted according to experiments, the curve effect is best; therefore, the invention takes 6 denoising automatic coding machines as an example to extract 100-dimensional features at each time point.
The second embodiment is as follows: the embodiment further defines the method for evaluating the performance degradation of the bearing based on the stacking denoising self-encoder and the self-organizing map, in the second step, the specific process of performing an absolute value taking operation on the input data x and normalizing the input data x to the [0,1] interval is as follows:
normalized in the manner of x*＝(x-xmin)/(xmax-xmin) Wherein x is*Is the value of input data x after the operation of taking absolute value and normalization, xmaxAnd xminRespectively, the maximum value and the minimum value of the absolute value of the vibration data at each time point of the input data x.
The third concrete implementation mode: this embodiment will be described with reference to fig. 3. The embodiment further defines the bearing performance degradation evaluation method based on the stacking denoising self-encoder and the self-organizing map, and the working principle of the first denoising self-encoder is as follows:
the working principle of the first denoising self-coding machine is as follows:
taking data of an original vibration signal as input data x of a first denoising self-encoding machine of a stacking denoising self-encoding network, wherein the first denoising self-encoding machine is used for mapping a function q randomlyDDestroying the input data x to obtain the data after adding noise
Figure GDA0002405550680000071
Through a coding process fθ1Generating output of a hidden layer
Figure GDA0002405550680000072
Output of hidden layer
Figure GDA0002405550680000073
Then go through decoding process gθ1'Generating reconstruction data z; the difference between the input data x and the reconstruction data z is taken as the reconstruction error LH(x, z) for training;
encoding process fθ1The specific process is as follows:
Figure GDA0002405550680000081
where s is a sigmoid activation function, W1Is the coding weight of the first denoise self-coder, b1The method comprises the steps that (1) the coding bias of a first denoising automatic coding machine is obtained, and theta 1 is a pre-trained coding parameter of the first denoising automatic coding machine;
θ1＝{W1,b1} (2)
decoding process gθ1'The specific process is as follows:
Figure GDA0002405550680000082
wherein W1' is the decoding weight of the first denoised self-coder of the coding network, b1'is a decoding bias of a first denoising auto-encoder of the encoding network, and theta 1' is a decoding parameter of the first denoising auto-encoder of the encoding network;
θ1'＝{W1',b1'} (4)
reconstruction error LH(x,z)＝||x-z||2Wherein, | | · | | represents a 2 norm;
applying an objective function by using a gradient descent algorithm
Figure GDA0002405550680000084
Minimizing, and improving the robustness of the features learned by the denoising self-coding machine from the input data x, wherein n is the number of samples, x(i)Is the ith sample data of the first image,
Figure GDA0002405550680000085
the data is the data of the ith sample data added with noise, i is 1,2, …, n;
the working principle of the second denoising self-coding machine, the third denoising self-coding machine, the fourth denoising self-coding machine, the fifth denoising self-coding machine and the sixth denoising self-coding machine is the same as that of the first denoising self-coding machine.
The fourth concrete implementation mode: the third embodiment further defines the method for evaluating the degradation of the performance of the bearing based on the stacking denoising self-encoder and the self-organizing map, which includes:
the third step is specifically as follows:
the structure of the stacking denoising self-encoder network is that an input layer is two hidden layers of the encoding network, an output layer of the encoding network, and two hidden layers of the decoding network are output layers of the decoding network;
after the network parameters of the stacking denoising self-encoder are randomly initialized, the training set data sequentially passes through the unsupervised pre-training of a first denoising self-encoder, a second denoising self-encoder and a third denoising self-encoder in the encoding network; output of a reserved hidden layer after pre-training of a first denoising self-coding machine is completed
Figure GDA0002405550680000097
And will imply the output of the layer
Figure GDA0002405550680000098
As the input of the second denoising self-coding machine, the output of the hidden layer is reserved after the pre-training of the second denoising self-coding machine is finished
Figure GDA0002405550680000099
And will imply the output of the layer
Figure GDA00024055506800000910
The third denoising self-coding machine is used as the input of the third denoising self-coding machine to complete the unsupervised pre-training of the training set data in the coding network;
the specific process of using the original data of the training set as the label and utilizing the BP algorithm to finely adjust the network parameters of the stacking denoising self-encoder network comprises the following steps:
let x be the original data of the training setmWhere M is 1,2, … M, where xmThe method comprises the steps of (1) training an mth original sample of original data of a training set, wherein the value range of M is 1-M; the hidden layer output of the first denoised self-coder of the stacked denoised self-coder network is
Figure GDA0002405550680000091
Outputting a first denoised hidden layer from an encoder
Figure GDA0002405550680000092
As the input of the second denoising autoencoder, the hidden layer output of the second denoising autoencoder is
Figure GDA0002405550680000093
By analogy, the output of the sixth denoising autoencoder of the stacked denoising autoencoder network is
Figure GDA0002405550680000094
Original sample x of training setmAs the label value, the error function phi (theta) is calculated
Figure GDA0002405550680000095
Wherein Θ ═ θ1,θ2,…,θ6The updating mode of the parameters is
Figure GDA0002405550680000096
Wherein α is the learning rate during parameter fine tuning;
and extracting the 100-dimensional characteristics of the coded network output by using the network parameters after fine adjustment.
The structure of the stacked denoising self-encoder network in the embodiment is 2560-;
100-500-1500 are respectively used to form a fourth denoising automatic encoder, a fifth denoising automatic encoder and a sixth denoising automatic encoder in the decoding network.
The fifth concrete implementation mode: the embodiment further defines the method for unsupervised construction of the bearing health factor curve based on the stacking denoising autoencoder and the self-organizing map, which is described in the fourth embodiment, and the fourth step specifically is as follows:
the specific process of iterative training is as follows:
step four, setting the number of neurons in a topological layer as d and the maximum training times T of the self-organizing mapping network;
step two, selecting 100-dimensional sample x at the kth time point of the training set datak＝{x1k,x2k,…,x100kIn which xkFor 100-dimensional samples of the extracted training set data at each time point, k is 1,2, …, p is the number of time points, xjkJ is the j-th neuron at the k-th time point of the input layer, 1,2, …, 100; inputting samples of all p time points into a self-organizing mapping network, wherein the number of input layer nodes of the self-organizing mapping network is consistent with the number of characteristics;
step four and three, after iterative training, calculating the vector representation and the input layer sample x of all d neurons in the topological layerkDistance d ofkSelect and xkThe neuron with the smallest distance is taken as the best matching neuron c, i.e. | | xk-wc||＝min{dk}，wcIs the vector characterization of the best matching neuron;
the vector representation formed by the connection weight between each neuron in the topology layer and the neuron of the input layer connected with the neuron is represented as wi'＝{wi'1,wi'2,…wi'100Where i' denotes the second of the topology layersi 'neurons, i' ═ 1,2, … d;
step four, updating the best matching neuron c and the connection weight of the neighborhood neuron and the input layer neuron of the best matching neuron c:
wi”j(t+1)＝wi”j(t)+η(t)·Ti”,c·(xjk-wi”j(t)) (6)
wherein, i 'is the ith' neighborhood neuron in the topological layer around the best matching neuron c, and t is the training times; w is ai”j(t +1) is input layer neuron x in t +1 training sessionsjkConnection weight value w between the adjacent neurons of ith' of topological layeri”j(t) is input layer neuron x for t training sessionsjkThe connection weight between adjacent neurons of the ith' neighborhood of the topological layer, η (T) is a gain function, 1 is more than η (T) is more than 0, η (T) is gradually reduced along with the increase of the training times, and Ti”,cIs a weight;
updating the weight T according to the distance value between the neighbor neuron of the best matching neuron c of the topological layer and the best matching neuron ci”,c＝exp(-Si”,c 2/2σ2) In which S isi”,cThe Euclidean distance between the ith' neighborhood neuron of the best matching neuron c of the topological layer and the best matching neuron c, and sigma is the standard deviation of the distance value between each neighborhood neuron of the best matching neuron c and the best matching neuron c;
step four, selecting a 100-dimensional sample at another time point to provide for an input layer of the self-organizing mapping network, and returning to the step four and the step three until all the samples in the training set are provided for the self-organizing mapping network;
step IV, assigning T +1 to the training times T, and returning to the step IV until the training times reach T;
step IV, after training, calculating the bearing health factor at each time point:
health value＝MQE＝||xk-wc|| (7)
wherein MQE is the minimum quantization error, the health value is the bearing health factor, and a bearing health factor curve on the training set is constructed according to the calculated bearing health factor.
The sixth specific implementation mode: the embodiment further defines the bearing performance degradation evaluation method based on the stacking denoising autoencoder and the self-organizing map, which is described in the fifth embodiment, and the specific process of smoothing the bearing health factor curve in the sixth step is as follows:
setting the filter window width to 15, returning a vector equal to the bearing health factor curve:
Figure GDA0002405550680000121
Figure GDA0002405550680000122
by the way of analogy, the method can be used,
Figure GDA0002405550680000123
y is bearing health factor data corresponding to each time point in the bearing health factor curve, and yy is the bearing health factor data corresponding to each time point after the bearing health factor curve is subjected to smoothing filtering;
normalizing the bearing health factor data corresponding to each time point after smoothing filtering treatment, yy*＝(yy-yymin)/(yymax-yymin)，yymaxAnd yyminRespectively the maximum value and the minimum value of the bearing health factor data corresponding to each time point in the normalized front bearing health factor curve, yy*The normalized values of the bearing health factors at various time points after the smoothing filtering process.
The time correlation (correlation) and monotonicity (monotonicity) are two commonly used indexes for evaluating the bearing health factor curve, the former represents the linear correlation degree of the health factor value of the bearing and the running time, and the latter measures the monotonous change trend condition of the bearing health factor curve, and the definitions are shown as a formula (8) and a formula (9).
Figure GDA0002405550680000124
Figure GDA0002405550680000131
Wherein the content of the first and second substances,
Figure GDA0002405550680000132
and lt'Respectively representing the value of the bearing health factor curve and the time value at the t' th time point,
Figure GDA0002405550680000133
is the average value of the corresponding curve values at various time points of the bearing health factor curve,
Figure GDA0002405550680000134
the average value of each time point value of the bearing health factor curve is obtained; t' is the full life cycle length of the bearing, and dF is the differential between the sequence values in the bearing health factor curve.
The PHM2012 challenge data are selected as experiment data of the original vibration signal to be evaluated in an algorithm, and Bearing1_1 and Bearing1_2 in the PHM2012 challenge data are used as training set data, and Bearing1_3 to Bearing1_7 in the PHM2012 challenge data are used as test set data.
The PHM2012 challenge data set is obtained by performing accelerated degradation experiments on bearings under different operating conditions by using a PRONOSTIA bearing test table, so as to obtain measured data of the bearings in the whole life cycle for fault detection, fault diagnosis and prediction related algorithm verification.
The measured data includes three conditions, i.e. condition 1: load 4000N, rotate speed 1800 r/min; working condition 2: the load is 4200N, and the rotating speed is 1650 r/min; working condition 3: load 5000N, and rotating speed 1500 r/min. The measured data under each working condition comprises a vibration signal and a temperature signal, and because partial data of the temperature signal is lost, the vibration signal in the whole life cycle of the bearing under the working condition 1 is adopted for carrying out experiments. The vibration signal comprises vibration information in the horizontal direction and the vertical direction, and is recorded every 10s, wherein the recording time is 0.1s every time, and comprises 2560 points;
as shown in Table 1, the vibration data of the original and cut-off working condition 1 is obtained, and the vibration value in the horizontal direction is adopted in the invention. Empirically, a vibration signal is considered to be invalid when it exceeds 20g, so vibration data other than that exceeding 20g is removed first. Taking Bearing1_3 as an example, the original vibration signal of the Bearing is shown in fig. 4.
Figure GDA0002405550680000141
TABLE 1
The original vibration signal data is input into the stacking denoising self-encoder network, the bearing health factor curve graph after smoothing processing shown in fig. 5 is obtained through the method of the invention, and the degradation condition of the bearing performance can be evaluated according to the obtained bearing health factor curve graph after smoothing processing and the working time condition of the bearing.
Compared with the traditional method, the method for constructing the bearing health factor curve improves the time relevance of the obtained bearing health factor curve by about 10 percent and improves the monotonicity by about 2.5 percent through algorithm verification.

Claims (2)

1. A bearing performance degradation evaluation method based on a stacking denoising autoencoder and self-organizing mapping is characterized by comprising the following specific steps:
acquiring original vibration signal data in the whole life cycle of a bearing by using a vibration sensor, and taking the original vibration signal data as input data x of a stacking denoising self-encoder;
step two, carrying out absolute value taking operation on the input data x in the step one, and normalizing the input data x to be in a [0,1] interval; the specific process comprises the following steps:
normalized in the manner of x*＝(x-xmin)/(xmax-xmin) Wherein x is*Is the value of input data x after the operation of taking absolute value and normalization, xmaxAnd xminNumber of vibrations at each time point of the input data x, respectivelyMaximum and minimum values according to absolute value;
using a part of vibration data in input data x as training set data, and using other part of vibration data in the input data x as test set data;
establishing a stacked denoising self-encoder network consisting of 6 denoising automatic encoders for performing feature extraction on training set data and test set data, wherein the first denoising self-encoder, the second denoising self-encoder and the third denoising self-encoder form an encoding network of the stacked denoising self-encoder network, and the fourth denoising self-encoder, the fifth denoising self-encoder and the sixth denoising self-encoder form a decoding network of the stacked denoising self-encoder network;
the working principle of the first denoising self-coding machine is as follows:
taking data of an original vibration signal as input data x of a first denoising self-encoding machine of a stacking denoising self-encoding network, wherein the first denoising self-encoding machine is used for mapping a function q randomlyDDestroying the input data x to obtain the data after adding noise
Figure FDA0002255941750000011
Figure FDA0002255941750000012
Through a coding process fθ1Generating output of a hidden layer
Figure FDA0002255941750000013
Output of hidden layer
Figure FDA0002255941750000014
Then go through decoding process gθ1'Generating reconstruction data z; the difference between the input data x and the reconstruction data z is taken as the reconstruction error LH(x, z) for training;
encoding process fθ1The specific process is as follows:
Figure FDA0002255941750000021
where s is a sigmoid activation function, W1Is the coding weight of the first denoise self-coder, b1The method comprises the steps that (1) the coding bias of a first denoising automatic coding machine is obtained, and theta 1 is a pre-trained coding parameter of the first denoising automatic coding machine;
θ1＝{W1,b1} (2)
decoding process gθ1'The specific process is as follows:
Figure FDA0002255941750000022
wherein W1' is the decoding weight of the first denoised self-coder of the coding network, b1'is a decoding bias of a first denoising auto-encoder of the encoding network, and theta 1' is a decoding parameter of the first denoising auto-encoder of the encoding network;
θ1'＝{W1',b1'} (4)
reconstruction error LH(x,z)＝||x-z||2Wherein, | | · | | represents a 2 norm;
applying an objective function by using a gradient descent algorithm
Figure FDA0002255941750000023
Minimizing, and improving the robustness of the features learned by the denoising self-coding machine from the input data x, wherein n is the number of samples, x(i)Is the ith sample data of the first image,
Figure FDA0002255941750000024
the data is the data of the ith sample data added with noise, i is 1,2, …, n;
the working principle of the second denoising self-coding machine, the third denoising self-coding machine, the fourth denoising self-coding machine, the fifth denoising self-coding machine and the sixth denoising self-coding machine is the same as that of the first denoising self-coding machine;
inputting the training set data determined in the second step into a stacking denoising self-encoderIn the encoding network of the network, the training set data is subjected to unsupervised pre-training of a first denoising self-encoding machine, a second denoising self-encoding machine and a third denoising self-encoding machine of the encoding network in sequence to obtain an encoding parameter theta 1 ═ W ═ of the first denoising self-encoding machine of the encoding network1,b1And coding parameters theta 2 ═ W of a second denoising self-coding machine2,b2W and the encoding parameter θ 3 ═ W of the third denoise self-encoder3,b3}；
Setting the encoding weight W of the fourth denoising self-encoding machine of the decoding network4Is W3Transpose of (2), the coding weight W of the fifth denoising autocoder5Is W2Transpose of (3), coding weight W of sixth denoising self-coder6Is W1Transposing; after the pre-training is finished, fine tuning is carried out on the network parameters of the stacking denoising self-encoder network by using a BP algorithm; obtaining 100-dimensional features output by the coding network by using the network parameters after fine tuning as 100-dimensional features extracted at each time point of training set data;
the method comprises the following steps: the structure of the stacked denoising self-encoder network comprises an input layer, two hidden layers of an encoding network, an output layer of the encoding network, two hidden layers of a decoding network and an output layer of the decoding network;
after the network parameters of the stacking denoising self-encoder are randomly initialized, the training set data sequentially passes through the unsupervised pre-training of a first denoising self-encoder, a second denoising self-encoder and a third denoising self-encoder in the encoding network; output of a reserved hidden layer after pre-training of a first denoising self-coding machine is completed
Figure FDA0002255941750000031
And will imply the output of the layer
Figure FDA0002255941750000032
As the input of the second denoising self-coding machine, the output of the hidden layer is reserved after the pre-training of the second denoising self-coding machine is finished
Figure FDA0002255941750000033
And will imply the output of the layer
Figure FDA0002255941750000034
The third denoising self-coding machine is used as the input of the third denoising self-coding machine to complete the unsupervised pre-training of the training set data in the coding network;
the specific process of using the original data of the training set as the label and utilizing the BP algorithm to finely adjust the network parameters of the stacking denoising self-encoder network comprises the following steps:
let x be the original data of the training setmWhere M is 1,2, … M, where xmThe method comprises the steps of (1) training an mth original sample of original data of a training set, wherein the value range of M is 1-M; the hidden layer output of the first denoised self-coder of the stacked denoised self-coder network is
Figure FDA0002255941750000035
Outputting a first denoised hidden layer from an encoder
Figure FDA0002255941750000036
As the input of the second denoising autoencoder, the hidden layer output of the second denoising autoencoder is
Figure FDA0002255941750000037
By analogy, the output of the sixth denoising autoencoder of the stacked denoising autoencoder network is
Figure FDA0002255941750000038
Original sample x of training setmAs the label value, the error function phi (theta) is calculated
Figure FDA0002255941750000041
Wherein Θ ═ θ1,θ2,…,θ6The updating mode of the parameters is
Figure FDA0002255941750000042
Wherein α is the learning rate during parameter fine tuning;
extracting 100-dimensional characteristics output by the coding network by using the network parameters after fine tuning;
step four, taking the 100-dimensional characteristics of each time point of the training set data extracted in the step three as the input of the self-organizing mapping network, wherein the number of input layer nodes of the self-organizing mapping network is consistent with the number of characteristics; after iterative training, calculating a bearing health factor of each time point of training set data, and further constructing a bearing health factor curve on the training set;
the specific process of iterative training is as follows:
step four, setting the number of neurons in a topological layer as d and the maximum training times T of the self-organizing mapping network;
step two, selecting 100-dimensional sample x at the kth time point of the training set datak＝{x1k,x2k,…,x100kIn which xkFor 100-dimensional samples of the extracted training set data at each time point, k is 1,2, …, p is the number of time points, xjkJ is the j-th neuron at the k-th time point of the input layer, 1,2, …, 100; inputting samples of all p time points into a self-organizing mapping network, wherein the number of input layer nodes of the self-organizing mapping network is consistent with the number of characteristics;
step four and three, after iterative training, calculating the vector representation and the input layer sample x of all d neurons in the topological layerkDistance d ofkSelect and xkThe neuron with the smallest distance is taken as the best matching neuron c, i.e. | | xk-wc||＝min{dk}，wcIs the vector characterization of the best matching neuron;
the vector representation formed by the connection weight between each neuron in the topology layer and the neuron of the input layer connected with the neuron is represented as wi'＝{wi'1,wi'2,…wi'100Where i ' denotes the i ' th neuron of the topological layer, i ' 1,2, … d;
step four, updating the best matching neuron c and the connection weight of the neighborhood neuron and the input layer neuron of the best matching neuron c:
wi”j(t+1)＝wi”j(t)+η(t)·Ti”,c·(xjk-wi”j(t)) (6)
wherein, i 'is the ith' neighborhood neuron in the topological layer around the best matching neuron c, and t is the training times; w is ai”j(t +1) is input layer neuron x in t +1 training sessionsjkConnection weight value w between the adjacent neurons of ith' of topological layeri”j(t) is input layer neuron x for t training sessionsjkThe connection weight between adjacent neurons of the ith' neighborhood of the topological layer, η (T) is a gain function, 1 is more than η (T) is more than 0, η (T) is gradually reduced along with the increase of the training times, and Ti”,cIs a weight;
updating the weight T according to the distance value between the neighbor neuron of the best matching neuron c of the topological layer and the best matching neuron ci”,c＝exp(-Si”,c 2/2σ2) In which S isi”,cThe Euclidean distance between the ith' neighborhood neuron of the best matching neuron c of the topological layer and the best matching neuron c, and sigma is the standard deviation of the distance value between each neighborhood neuron of the best matching neuron c and the best matching neuron c;
step four, selecting a 100-dimensional sample at another time point to provide for an input layer of the self-organizing mapping network, and returning to the step four and the step three until all the samples in the training set are provided for the self-organizing mapping network;
step IV, assigning T +1 to the training times T, and returning to the step IV until the training times reach T;
step IV, after training, calculating the bearing health factor at each time point:
health value＝MQE＝||xk-wc|| (7)
MQE is the minimum quantization error, the health value is a bearing health factor, and a bearing health factor curve on the training set is constructed according to the calculated bearing health factor;
inputting test set data into a trained stacked self-encoder network by using the method in the third step, extracting features through a plurality of hidden layers, inputting the extracted features of the test set into the trained self-organizing map network by using the method in the fourth step, calculating to obtain a bearing health factor corresponding to each time point on the test set, and constructing a bearing health factor curve on the test set;
and step six, smoothing the bearing health factor curves constructed in the step four and the step five respectively to obtain smoothed bearing health factor curves, and evaluating the performance degradation condition of the bearing by using the smoothed bearing health factor curves.
2. The method for evaluating the performance degradation of the bearing based on the stacked denoising self-encoder and the self-organizing map as claimed in claim 1, wherein the smoothing process of the bearing health factor curve in the sixth step is specifically as follows:
setting the filter window width to 15, returning a vector equal to the bearing health factor curve: yy (1) is y (1),
Figure FDA0002255941750000061
Figure FDA0002255941750000062
by the way of analogy, the method can be used,
Figure FDA0002255941750000063
y is bearing health factor data corresponding to each time point in the bearing health factor curve, and yy is the bearing health factor data corresponding to each time point after the bearing health factor curve is subjected to smoothing filtering;
normalizing the bearing health factor data corresponding to each time point after smoothing filtering treatment, yy*＝(yy-yymin)/(yymax-yymin)，yymaxAnd yyminAre respectively normalizedMaximum and minimum values of bearing health factor data, yy, corresponding to each time point in the front bearing health factor curve*The normalized values of the bearing health factors at various time points after the smoothing filtering process.
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