Rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and sparse laminated automatic encoder

Abstract

The invention discloses a rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and a sparse laminated automatic encoder. According to the method of the invention, firstly a smart mobile phone is used for acquiring a sound signal of the rolling bearing fault; then short-time Fourier analysis is performed on the sound signal for obtaining a spectrogram matrix; then the modulus value of the matrix is acquired and gray scale normalization processing is performed; then the normalized data are selected and input into a deep studying network for automatically extracting characteristics; and finally the characteristic which is extracted by a neural net is input into a Softmax classifier for identifying the fault mode. The invention provides the rolling bearing sound signal fault diagnosis method based on smart mobile phone sound signal short-time Fourier transform (STFT) and the sparse laminated automatic encoder (SAE). Through testing result analysis, the rolling bearing sound signal fault diagnosis method can accurately determine the fault mode of the rolling bearing.

Description

technical field [0001] The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and sparse stacked automatic encoder. Background technique [0002] Rolling bearings are one of the standard parts widely used in various mechanical equipment, and rolling bearing failures are also one of the most important causes of machine failures. According to statistics, about 30% of rotating machinery failures are related to rolling bearing failures. [0003] During routine maintenance on railroad systems, workers hit the wheels of locomotives with hammers, and the sound of the knocking can tell if there are cracks in the wheels. In the same way, experienced maintenance personnel in many engineering fields can judge whether the machine is operating normally based on the sound characteristics of the machine when it is working. The underlying physics is that d...

AI Technical Summary

Problems solved by technology

[0006] Smartphones have become an important part of our daily life. Although we are already familiar with the recording function of smartphones, it is still rare to use them as data acquisition sensors for fault information in equipment fault diagnosis.

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