Sparse self-encoding rolling bearing fault diagnosis method

Abstract

The invention discloses a sparse self-encoding rolling bearing fault diagnosis method. The method specifically comprises the following steps: S1, acquiring original vibration data of a rolling bearingin each fault state, performing linear projection on each kind of vibration data through compressed sensing, and combining compressed signals after linear projection of each fault type into a multi-fault type low-dimensional compressed signal matrix; S2, determining wavelet packet energy entropy of the multi-fault type low-dimensional compressed signal matrix to form a feature vector matrix for bearing fault diagnosis; S3, inputting the feature vector matrix of the rolling bearing under multiple fault types into a sparse automatic encoder for training, and further extracting the weight from an input layer to a hidden layer as a feature matrix; and S4, classifying features extracted by a sparse automatic coding neural network through a neural network classifier, and finishing fault diagnosis classification of the rolling bearing. According to the method, the diagnosis complexity is reduced, the diagnosis time is shortened, and the high diagnosis precision is ensured.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing fault diagnosis and relates to a sparse self-coding rolling bearing fault diagnosis method based on compressed sensing and wavelet packet energy entropy. Background technique [0002] Rolling bearings are the key basic parts and important rotating parts of the equipment manufacturing industry, and are called mechanical joints. It has the advantages of high efficiency, small frictional resistance and easy lubrication, etc. It is widely used in rotating machinery. However, rolling bearings are also the most prone to failure components in rotating machinery. According to statistics, they account for a high proportion of all kinds of failures, about 30%. Bearing vibration signals are non-stationary and nonlinear, and noise is complex and changeable in modern industry. It is becoming more and more difficult to identify bearing faults accurately and quickly with traditional diagnostic methods. ...

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Problems solved by technology

Third, when the dimension of the sensitive feature space is high, it takes a long time to diagnose the fault of the rotating bearing with the intelligent diagnosis algorithm

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