A Sparse Feature Extraction Method for Fault Signals of Rotating Machinery

Abstract

The invention provides a method for extracting sparse features of rotating machinery fault signals. According to the fault generation mechanism of rotating machinery shock faults, an over-complete atomic library of faults is constructed; Resolution generalized S transform and normalization processing to calculate the multi-resolution time spectrum of the signal to be analyzed; calculate the maximum value of the normalized multi-resolution time spectrum, and combine with the orthogonal matching pursuit algorithm to calculate a set of most sparse It represents the atomic set of the signal to be observed; according to the sparse representation signal of the fault and the fault characteristic frequency and speed information of the equipment, the fault type is determined, and the rapid fault diagnosis of the mechanical equipment is realized. The advantages are that the complexity and search time of atomic search are greatly reduced, the efficiency of sparse decomposition is improved, and the fault diagnosis efficiency of impact faults such as cracks, pitting or spalling is improved.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rotating machinery, in particular to a method for extracting sparse features of fault signals of rotating machinery. Background technique [0002] During the operation of mechanical equipment, rotating parts are the key equipment to ensure the safe and stable operation of the equipment. Once a failure occurs, the light one will affect the production accuracy, and the serious one will cause major equipment failure and huge economic losses. There are two main types of vibration signals of rotating machinery, one is the smooth fault signal caused by wear, misalignment, unbalance, etc., and the other is the non-stationary impact fault signal caused by cracks, pitting, peeling, etc. Shock faults are more likely to cause equipment breakdown in a short period of time. Therefore, the feature extraction and fault diagnosis of shock faults are of great significance. [0003] The traditional Fourier analys...

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Problems solved by technology

[0005] However, there are two major defects in the above-mentioned patents: ① The window width of the short time window is fixed and not adaptive, especially for oscillation attenuation signals, the selection of the short time window directly affects the time-frequency resolution of the time spectrum ② The harmonic estimation algorithm involved in the recognition of non-periodic and sparse fault signals under intermittent working conditions is not good, and the feature extraction is difficult

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