Mechanical fault diagnosis method for universal circuit breaker based on vibration-acoustic signal feature fusion

Abstract

The invention provides a universal circuit breaker mechanical fault diagnosis method based on feature fusion of vibration and sound signals. The method includes steps of 1, collecting machine vibration signals and machine sound signals during an engaging and disengaging process of a universal circuit breaker; 2, adopting an improved wavelet packet threshold value denoising algorithm for denoising; 3, adopting a complementary total average empirical mode decomposition algorithm for extracting a plurality of solid mode function components reflecting state information of engagement and disengagement actions of the circuit breaker from the denoising signals; 4, determining the number Z of the solid mode function components; 5, calculating the energy ratio, the sample ratio and the power spectrum entropy as three types of features; 6, adopting a combination core principal component analysis method for performing dimension reduction on a feature sample with unified three types of features of the vibration and the sound signals and obtaining M principle components; 7, establishing a related vector machine based sequence binary tree multiple classifier model.

Description

technical field [0001] The technical solution of the present invention relates to the mechanical fault diagnosis of circuit breakers, in particular to a fault diagnosis method based on fusion of vibration-acoustic signal features. Background technique [0002] According to the country's plan for smart grid construction, smart substations are an important part and key link of smart grids. Intelligent universal circuit breakers are an important part of smart substations and one of the key equipment for low-voltage distribution networks in power systems. It is used at the main incoming line or important equipment in the low-voltage power distribution system. Research at home and abroad shows that circuit breakers are prone to mechanical failures, and the research work on them mainly focuses on the diagnosis of mechanical failures of high-voltage circuit breakers, but the reliability of the operation of universal circuit breakers is also related to the safe operation of power sy...

AI Technical Summary

Problems solved by technology

[0004] In the feature extraction process of the second link, in view of the nonlinear and non-stationary characteristics of vibration signals, wavelet analysis, empirical mode decomposition (empirical modedecomposition, EMD), and overall Average empirical mode decomposition (ensemble EMD, EEMD), Hilbert-Huang transform (Hilbert-Huang transform, HHT), etc., but wavelet has its own defects in fault signal decomposition, and its decomposition effect depends on wavelet base and decomposition scale The selection of is not self-adaptive. In addition, there is energy leakage in wavelet decomposition

[0006] To sum up, the existing fault identification methods all have certain defects. In the prior art, there is an urgent need to provide a mechanical fault diagnosis method for universal circuit breakers with high fault identification rate and good stability based on the fusion of vibration and acoustic signal features.

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