Gear fault diagnosis method based on orthogonal match between multiple parallel dictionaries

Abstract

The invention relates to a gear fault diagnosis method based on the orthogonal match between multiple parallel dictionaries. According to the method, gear vibration signals are expressed in the mode of linear superposition of simple and sparse atoms of the multiple parallel dictionaries. As for the multiple parallel dictionaries, Fourier dictionaries and impact time frequency dictionaries are selected to form the multiple dictionaries according to the characteristics of the gear vibration signals, matched atoms are selected in parallel in all sub-dictionaries with a genetic algorithm, coefficients of all orders are compared to obtain a most matched atom, Gram-Schmidt orthogonalization is performed on the atom, and then a new atom library is formed. Analysis signals are projected to the atom library, and the projections are subtracted from the signals to form residual signals to be decomposed the next time. The decomposition process is completed after the iteration end conditions are met, the matched atoms and the matching coefficient are extracted, the matched atoms based on the impact time frequency dictionaries are reconstructed, corresponding impact components can be obtained, and then fault information of the gear vibration signals is demodulated and extracted for fault diagnosis.

Description

technical field [0001] The invention relates to a gear fault diagnosis method, in particular to a gear fault diagnosis method based on parallel multi-dictionary orthogonal matching, and belongs to the technical field of fault diagnosis. Background technique [0002] As an indispensable general component for connecting and transmitting power in rotating mechanical equipment, gears are of great significance to the detection and fault diagnosis of their running states. The fault vibration signal is a typical type of non-stationary and nonlinear signal. Compared with the stationary signal, its distribution parameters or distribution rules change with time, and these signals are often contacted in engineering practice. Research is of great significance for engineering applications. [0003] In the analysis of non-stationary signals, time-frequency analysis methods show certain advantages. Typical time-frequency analysis methods include short-time Fourier transform, Wigner-Ville ...

AI Technical Summary

Problems solved by technology

[0004] The structural components of the gear fault vibration signal are complex, and have a lot of noise and transient features. The rich atomic library information is very important for the separation of different structural components and the extraction of fault features, but this will inevitably increase the calculation of the algorithm and affect the reconstruction accuracy. make higher demands

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