Intelligent fault diagnosis method for rolling bearing

Abstract

The invention discloses an intelligent fault diagnosis method for a rolling bearing. The method comprises the steps of obtaining a vibration test signal of the rolling bearing by utilizing an acceleration sensor; performing decomposition and reconstruction on the vibration signal by adopting wavelet package transform to obtain reconstructed signals of all frequency bands; performing singular value decomposition on the reconstructed signals by applying a singular value decomposition technology, and combining singular values of all the frequency bands into eigenvectors of the rolling bearing; and forming an eigenmatrix by the eigenvectors of the rolling bearing in different states, inputting the eigenmatrix to a PNN neural network for performing training, performing feature extraction on the vibration signal, and inputting the vibration signal to the trained PNN neural network for performing fault mode identification. According to the method, feature information contained in the vibration signal of the rolling bearing can be accurately extracted and is accurately identified and distinguished through the PNN neural network; and the method is high in diagnosis speed, high in precision and high in reliability.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method, the core of which is the rolling bearing fault feature extraction technology based on wavelet packet transformation and singular value decomposition and the pattern recognition technology based on PNN neural network. Background technique [0002] Rolling bearings are one of the key components of rotating machinery. During use, they are subjected to a series of physical effects such as mechanical stress and wear, which cause deformation, corrosion and other damage to the bearings. At the same time, due to the non-standard processing and manufacturing process and improper assembly, the bearing will also be artificially damaged, and the accumulation and deepening of these damages will eventually lead to the failure of the bearing. Once the bearing fails, it will cause the unit to stop, or even a major production accident. Therefore, the fault diagnosis of rolling bearings is very importan...

AI Technical Summary

Problems solved by technology

Wavelet transform is one of the commonly used methods in signal time-frequency analysis at present, but it can only decompose low-frequency signals, and does not process high-frequency parts of signals, and the fault characteristic information of rolling bearings is often concentrated in the middle and high frequencies of signals. segment, which limits the application of wavelet transform in rolling bearing fault diagnosis

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