CN112861275A - Rotary machine fault diagnosis method based on minimum information entropy feature learning model - Google Patents

Rotary machine fault diagnosis method based on minimum information entropy feature learning model Download PDF

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CN112861275A
CN112861275A CN202110030109.XA CN202110030109A CN112861275A CN 112861275 A CN112861275 A CN 112861275A CN 202110030109 A CN202110030109 A CN 202110030109A CN 112861275 A CN112861275 A CN 112861275A
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陆建涛
张宗振
李舜酩
马会杰
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention provides a rotary machine fault diagnosis method based on a minimum information entropy feature learning model, which comprises the following steps of: constructing a Hankel matrix, stacking the Hankel matrix, and obtaining a characteristic matrix through linear mapping; to rows of the feature matrixL 2Normalizing the norm, and then minimizing the information entropy of the feature matrix to obtain a trained weight matrix; solving the characteristics of the fault vibration signal through the trained weight matrix, standardizing the fault vibration signal by Z-score, combining the fault vibration signal with the label, and importing the fault vibration signal into a Softmax model for optimization to obtain a trained Softmax classifier; and (3) calculating the characteristics of the vibration signals of the rotary machine under the test environment through the weight matrix, standardizing the vibration signals by Z-score, and inputting the standardized vibration signals into a Softmax classifier to obtain a fault diagnosis result of the rotary machine. The invention is characterized in thatCompared with the existing unsupervised feature extraction method, the method has better noise adaptability, accuracy and robustness.

Description

Rotary machine fault diagnosis method based on minimum information entropy feature learning model
Technical Field
The invention relates to the technical field of intelligent fault diagnosis of vibration signals, in particular to a rotary machine fault diagnosis method based on a minimum information entropy feature learning model.
Background
With the development of modern industry and the progress of science and technology, loading tools such as engineering vehicles, rail transit and the like are developing towards high speed, high precision and high efficiency, and establishing a reliable health monitoring system is a necessary measure for ensuring the high-efficiency and safe operation of the equipment, and has important significance for reasonably prolonging the service life of mechanical equipment, reducing the periodic maintenance cost and ensuring the safety of the operation of the equipment. As a main part for transmitting power, the gear box has the characteristics of compact structure, high transmission precision and large transmission torque, and is widely applied to the fields of aviation, engineering vehicles, rail transit and the like. The rotating parts in the gear box, such as a shaft, a bearing, a gear and the like, have the characteristics of high processing precision, small transmission error, high equipment precision and the like, work under various complex and changeable working conditions and running environments, are the most critical parts of the gear box and parts which are easy to break down, and fault signals are often submerged in environmental noise and are not easy to perceive.
The establishment of a reliable health monitoring system is a necessary measure for ensuring the high-efficiency and safe operation of the equipment, and has important significance for reasonably prolonging the service life of mechanical equipment, reducing periodic maintenance cost and ensuring the safety of the operation of the equipment. Due to the rapid development of computer networks, the scale of equipment groups for mechanical health monitoring is large, the number of required measuring points is large, the data collection duration is long, and massive data are acquired, so that the mechanical health monitoring system is promoted to enter a big data era. The traditional fault diagnosis method is incapable of coping with the fault, an advanced data driving method is researched and utilized, information is extracted from big mechanical data, the health condition of equipment is accurately and efficiently identified, and the fault diagnosis method becomes a new hotspot problem in the field of mechanical fault diagnosis. The mechanical health monitoring method based on deep learning realizes the organic combination of unsupervised learning and supervised learning, can simultaneously complete the self-adaptive extraction of big data fault characteristics and the identification of mechanical health conditions, and overcomes the defects of the traditional method in characteristic extraction and fault identification.
Most deep learning algorithms have a lot of hyper-parameters to be adjusted, the adjusting processes need to be experienced a priori, and meanwhile, the feature generalization capability in a noise environment is poor.
Disclosure of Invention
In order to solve the problems, the invention provides a rotary machine fault diagnosis method based on a minimum information entropy feature learning model.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a rotary machine fault diagnosis method based on a minimum information entropy feature learning model comprises the following steps:
constructing a Hankel matrix according to the collected fault vibration signals, stacking the Hankel matrix, and then obtaining a characteristic matrix through linear mapping;
carry out L to the line of the characteristic matrix2Normalizing the norm, and then minimizing the information entropy of the feature matrix to obtain a trained weight matrix;
the method comprises the steps of solving the characteristics of fault vibration signals through a trained weight matrix, carrying out Z-score standardization on the characteristics of the fault vibration signals, combining the characteristics with labels, importing the characteristics into a Softmax model for optimization, and obtaining a Softmax classifier after training;
and calculating the characteristics of the rotary mechanical vibration signal under the test environment through a weight matrix, carrying out Z-score standardization on the characteristics of the rotary mechanical vibration signal, and inputting the characteristics into a Softmax classifier to obtain a fault diagnosis result of the rotary machine.
Further, the process of constructing the Hankel matrix in step 1 is as follows: setting M samples of the rotary mechanical vibration signal with the label as
Figure BDA0002891766950000021
Hankel matrix for constructing fault vibration signal according to rotating speed information of rotating machinery
Figure BDA0002891766950000022
Wherein N isinIs the length of the matrix array, NsIs the length of the matrix row; and stacking Hankel matrixes of all the rotating mechanical vibration samples and linearly mapping to obtain an input matrix S of the training model, wherein,
Figure BDA0002891766950000023
further, the process of minimizing the information entropy of the feature matrix in step 2 is: first minimizing the cost function J(W)So as to extract the distinctive features of the image,
Figure BDA0002891766950000024
wherein,
Figure BDA0002891766950000025
for the normalized features of the ith row and the jth column in the input matrix, the row normalization mode of the feature matrix is
Figure BDA0002891766950000026
fiThe characteristics of the ith row of the activated fault signal characteristic matrix; the feature activation function of the feature matrix is f ═ WS, where W is the weight matrix,
Figure BDA0002891766950000031
Noutthe length of a matrix array in the weight matrix; and optimizing an objective function through an L-BFGS algorithm to train a weight matrix.
Further, the implementation process of training the Softmax classifier in step 3 is as follows: extracting the characteristics of the fault vibration signal through a characteristic activation function and a trained weight matrix; the features are then Z-score normalized, i.e. fz＝(f-μf)-σfWherein f iszFor normalized features, f is the non-normalized fault vibration signal feature, μfFor non-normalized fault vibration signal characteristic fValue σfThe standard deviation of the non-normalized fault vibration signal characteristic f is obtained; adding a label to become
Figure BDA0002891766950000032
Wherein
Figure BDA0002891766950000033
Normalized feature of the ith sample, yiA failure label for the ith sample; and then training the Softmax training model to train a weight vector theta of the Softmax classifier so as to obtain the trained Softmax classifier.
The method extracts the sample characteristics through the minimum information entropy characteristic learning algorithm, and enables the fault diagnosis method to be more accurate and reasonable through the Z-score standardization of the Hankel matrix and the characteristics. The method comprises the steps of firstly training a minimum information entropy characteristic learning model through existing label data, then inputting a vibration signal obtained through testing into the trained model in the actual rotating machine health detection process, and carrying out fault diagnosis on the rotating machine. The method has better accuracy, robustness and noise adaptability.
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FIG. 1 is a flow chart of a method in an embodiment of the present invention.
FIG. 2 is a graph showing the comparison of the test effects in example 1 of the present invention.
FIG. 3 is a comparison chart of the test effects in example 2 of the present invention.
Detailed Description
The specific contents of the present invention will be further described with reference to the accompanying drawings and examples.
The embodiment of the invention provides a fault diagnosis method for a rotary machine, which comprises the following specific steps as shown in fig. 1:
constructing a Hankel matrix according to the collected fault vibration signals, stacking the Hankel matrix, and then obtaining a characteristic matrix through linear mapping;
carry out L to the line of the characteristic matrix2Normalizing the norm, and then minimizing the information entropy of the feature matrix to obtain a trained weight matrix;
the method comprises the steps of solving the characteristics of fault vibration signals through a trained weight matrix, carrying out Z-score standardization on the characteristics of the fault vibration signals, combining the characteristics with labels, importing the characteristics into a Softmax model for optimization, and obtaining a Softmax classifier after training;
and calculating the characteristics of the rotary mechanical vibration signal under the test environment through a weight matrix, carrying out Z-score standardization on the characteristics of the rotary mechanical vibration signal, and inputting the characteristics into a Softmax classifier to obtain a fault diagnosis result of the rotary machine.
The process of constructing the Hankel matrix in step 1 is as follows: setting M samples of the rotary mechanical vibration signal with the label as
Figure BDA0002891766950000041
Hankel matrix for constructing fault vibration signal according to rotating speed information of rotating machinery
Figure BDA0002891766950000042
Wherein N isinIs the length of the matrix array, NsIs the length of the matrix row; and stacking Hankel matrixes of all the rotating mechanical vibration samples and linearly mapping to obtain an input matrix S of the training model, wherein,
Figure BDA0002891766950000043
the process of minimizing the information entropy of the feature matrix in step 2 is: first minimizing the cost function J(W)So as to extract the distinctive features of the image,
Figure BDA0002891766950000044
wherein,
Figure BDA0002891766950000045
for the normalized features of the ith row and the jth column in the input matrix, the row normalization mode of the feature matrix is
Figure BDA0002891766950000046
fiThe characteristics of the ith row of the activated fault signal characteristic matrix; the feature activation function of the feature matrix is f ═ WS, where W is the weight matrix,
Figure BDA0002891766950000047
Noutthe length of a matrix array in the weight matrix; and optimizing an objective function through an L-BFGS algorithm to train a weight matrix.
The implementation process of training the Softmax classifier in the step 3 is as follows: extracting the characteristics of the fault vibration signal through a characteristic activation function and a trained weight matrix; the features are then Z-score normalized, i.e. fz＝(f-μf)-σfWherein f iszFor normalized features, f is the non-normalized fault vibration signal feature, μfIs the mean, σ, of the non-normalized fault vibration signal characteristic ffThe standard deviation of the non-normalized fault vibration signal characteristic f is obtained; adding a label to become
Figure BDA0002891766950000048
Wherein
Figure BDA0002891766950000049
Normalized feature of the ith sample, yiA failure label for the ith sample; and then training the Softmax training model to train a weight vector theta of the Softmax classifier so as to obtain the trained Softmax classifier.
Example 1:
and the data adopts bearing fault data disclosed by Kaiser university to carry out model training and testing. The fault types are { normal, inner ring fault (0.18mm, 0.36mm, 0.53mm), outer ring fault (0.18mm, 0.36mm, 0.53mm), rolling element fault (0.18mm, 0.36mm, 0.53mm) }, each fault type has four loads (1797r/min, 1772r/min, 1750r/min, 1730r/min), the sampling frequency is 12kHz data, the number of vibration sample points of each fault type is 1200, and the measurement is repeated 100 times. Thus a total of 4000 samples. 10% of these samples were taken as training samples, and the rest were test samples. First, set up trainingA sample matrix, wherein a Hankel matrix (the matrix length is 100) of each training sample is stacked to be used as a training sample matrix, training is carried out through a minimum entropy characteristic learning model, and the output dimension N isoutThe weight matrix W is trained to 100.
And multiplying the Hankel matrix of the training sample by W, carrying out Z-score normalization, combining with the label, carrying out Softmax model training, and testing by using the test sample after training. The accuracy of the final test reaches more than 99%. In order to illustrate the robustness of the intelligent fault diagnosis method based on the minimum information entropy feature learning model and the influence of the Hankel matrix and the feature normalization, the method, the minimum entropy model without feature normalization and the standard sparse filtering are compared, and the obtained result is shown in figure 2.
Example 2:
and the data adopts gearbox fault data to train and test the model. Data are collected by an acceleration sensor, and the sampling frequency is 12800 Hz. The fault types are { normal, abrasion, pitting and tooth breakage }, the vibration sample points of each fault type are 1280, and 100 times of repeated measurement are carried out. Thus there are a total of 400 samples. Firstly, establishing a training matrix sample matrix, stacking a Hankel matrix (the length of the matrix is 100) of each training sample as a training sample matrix, training through a minimum entropy characteristic learning model, and outputting a dimension NoutThe weight matrix W is trained to 100.
And multiplying the Hankel matrix of the training sample by W, carrying out Z-score normalization, combining with the label, carrying out the training of a Softmax feature classifier, and testing by using the test sample after the training is finished, wherein the accuracy of the final test reaches more than 99%.
In order to illustrate the accuracy and robustness of the proposed intelligent fault diagnosis method based on the minimum information entropy feature learning model, samples with different percentages are compared with standard sparse filtering as training sets, and the obtained result is shown in fig. 3.

Claims (4)

1. A rotary machine fault diagnosis method based on a minimum information entropy feature learning model is characterized by comprising the following steps:
constructing a Hankel matrix according to the collected fault vibration signals, stacking the Hankel matrix, and then obtaining a characteristic matrix through linear mapping;
carry out L to the line of the characteristic matrix2Normalizing the norm, and then minimizing the information entropy of the feature matrix to obtain a trained weight matrix;
the method comprises the steps of solving the characteristics of fault vibration signals through a trained weight matrix, carrying out Z-score standardization on the characteristics of the fault vibration signals, combining the characteristics with labels, importing the characteristics into a Softmax model for optimization, and obtaining a Softmax classifier after training;
and calculating the characteristics of the rotary mechanical vibration signal under the test environment through a weight matrix, carrying out Z-score standardization on the characteristics of the rotary mechanical vibration signal, and inputting the characteristics into a Softmax classifier to obtain a fault diagnosis result of the rotary machine.
2. The rotating machine fault diagnosis method based on the minimum information entropy feature learning model of claim 1, characterized in that: the process of constructing the Hankel matrix in step 1 is as follows: setting M samples of the rotary mechanical vibration signal with the label as
Figure FDA0002891766940000011
Hankel matrix for constructing fault vibration signal according to rotating speed information of rotating machinery
Figure FDA0002891766940000012
Wherein N isinIs a matrixLength of column, NsIs the length of the matrix row; and stacking Hankel matrixes of all the rotating mechanical vibration samples and linearly mapping to obtain an input matrix S of the training model, wherein,
Figure FDA0002891766940000013
3. the rotating machine fault diagnosis method based on the minimum information entropy feature learning model of claim 2, characterized in that: the process of minimizing the information entropy of the feature matrix in step 2 is: first minimizing the cost function J(W)So as to extract the distinctive features of the image,
Figure FDA0002891766940000014
wherein,
Figure FDA0002891766940000018
for the normalized features of the ith row and the jth column in the input matrix, the row normalization mode of the feature matrix is
Figure FDA0002891766940000016
fiThe characteristics of the ith row of the activated fault signal characteristic matrix; the feature activation function of the feature matrix is f ═ WS, where W is the weight matrix,
Figure FDA0002891766940000017
Noutthe length of a matrix array in the weight matrix; and optimizing an objective function through an L-BFGS algorithm to train a weight matrix.
4. The rotating machine fault diagnosis method based on the minimum information entropy feature learning model of claim 3, characterized in that: the implementation process of training the Softmax classifier in the step 3 is as follows: extracting the characteristics of the fault vibration signal through the characteristic activation function and the trained weight matrix(ii) a The features are then Z-score normalized, i.e. fz＝(f-μf)/σfWherein f iszF is the features of the fault vibration signal which is not normalized, carry forwardfIs the mean, σ, of the non-normalized fault vibration signal characteristic ffThe standard deviation of the non-normalized fault vibration signal characteristic f is obtained; adding a label to become
Figure FDA0002891766940000021
Wherein
Figure FDA0002891766940000022
Normalized feature of the ith sample, yiA failure label for the ith sample; and then training the Softmax training model to train a weight vector theta of the Softmax classifier so as to obtain the trained Softmax classifier.
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CN113449783A (en) * 2021-06-17 2021-09-28 广州大学 Countermeasure sample generation method, system, computer device and storage medium
CN113743585A (en) * 2021-08-17 2021-12-03 山东科技大学 Rotary machine early fault diagnosis method based on rapid nonlinear blind deconvolution

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CN108509701A (en) * 2018-03-21 2018-09-07 南京航空航天大学 A kind of direct intelligent diagnosing method of rotating machinery fault based on vibration signal
CN108827605A (en) * 2018-03-20 2018-11-16 南京航空航天大学 A kind of mechanical breakdown characteristic automatic extraction method based on improvement sparseness filtering

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Publication number Priority date Publication date Assignee Title
CN106017876A (en) * 2016-05-11 2016-10-12 西安交通大学 Wheel set bearing fault diagnosis method based on equally-weighted local feature sparse filter network
CN108225750A (en) * 2017-12-12 2018-06-29 南京航空航天大学 A kind of rotary machinery fault diagnosis method based on the more correlations of fault signature
CN108827605A (en) * 2018-03-20 2018-11-16 南京航空航天大学 A kind of mechanical breakdown characteristic automatic extraction method based on improvement sparseness filtering
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113449783A (en) * 2021-06-17 2021-09-28 广州大学 Countermeasure sample generation method, system, computer device and storage medium
CN113743585A (en) * 2021-08-17 2021-12-03 山东科技大学 Rotary machine early fault diagnosis method based on rapid nonlinear blind deconvolution
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