Rolling bearing fault diagnosis method

Abstract

The invention relates to a rolling bearing fault diagnosis method. The method uses a learning algorithm of a CNN (Convolutional Neural Network) theory to complete a feature extraction task needed by fault diagnosis, and does not rely one manual selection, intrinsic features of input data are extracted automatically from simple to complex and from low-level to high-level, and abundant information hidden in known data can be dug out automatically; and a support vector regression method is used to identity a test sample in a classifying manner, support vector regression with a high generalization capability can be used to identity an unknown new sample in higher precision, and the disadvantage that a default classifier of deep learning tends to be low in the generalization capability can be overcome when support vector regression serves as a classifier to identify samples in the classified manner. The rolling bearing fault diagnosis accuracy and validity can be improved, a new effective approach is provided for solving problems in rolling bearing fault diagnosis, and the method of the invention can be widely applied to fault analysis of complex mechanical systems in the fields of chemical industry, metallurgy, electric power, aviation and the like.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis and computer artificial intelligence, and in particular relates to a rolling bearing fault diagnosis method based on convolutional neural network and support vector regression. Background technique [0002] Rolling bearings are one of the most important key components in rotating machinery. Rolling bearings are widely used in various important fields such as chemical industry, metallurgy, electric power, aviation, etc., but at the same time, they are often in harsh working environments such as high temperature, high speed, and heavy load. As a result, rolling bearings are one of the most vulnerable elements. The performance and working conditions of the bearing directly affect the performance of the shaft associated with it, the gears installed on the shaft, and even the entire machine equipment. Its defects will cause abnormal vibration and noise of the equipment, and even caus...

AI Technical Summary

Problems solved by technology

[0004] As a linear time-frequency analysis method, Fourier transform can process signals clearly and quickly, and has a certain time-frequency resolution. Its flexibility and practicability are more prominent. The resolution is zero, and it has uncertainty for nonlinear and non-stationary signals, which limits its application range

The FFT method cannot take into account the overall picture and localization of the signal in the time domain and frequency domain at the same time

Wavelet transform can perform localized analysis on time and frequency, achieve time subdivision at high frequency, subdivide frequency at low frequency, and analyze time-frequency signal adaptively, but the wavelet base is different, the decomposition results are different, and the wavelet base is more difficult to choose

The EMD method can decompose the signal into multiple IMF (IntrinsicMode Function, Intrinsic Mode Function) components, and perform Hilbert transformation on all IMF components to obtain the time-frequency distribution of the signal, but there are still some problems in theory, such as in the EMD method The modal confusion, under-envelope, over-envelope, endpoint effect and other issues are all under research

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