Fault Diagnosis for Automata Based on Local Wave Noise Reduction and Bispectral Analysis

doi:10.3969/j.issn.1000-1093.2014.07.022

Abstract

Abstract: Feature extraction is a key of mechanical fault diagnosis, which directly affects the accuracy of fault diagnosis and the reliability of early prediction. The vibration signal components of automata surface are complex, including rich information on the motion states of components and parts, and a lot of noise components. Interference information should be effectively removed in order to make a correct assessment and analysis of the signal. The vibration signal of automata surface has obvious short impact characteristics, and is a typical non-Gaussian, nonlinear signal. Bispectral analysis has certain advantages especially in dealing with non-Gaussian signal and identifying nonlinear system failures. Automata vibration signal is noise-reduced and analyzed by the local wave theory and the Bispectral analysis.

Key words: oscillation and wave, local wave, noise reduction, bispectral analysis, automata

CLC Number:

TK42

PAN Hong-xia, LAN Hai-long, REN Hai-feng. Fault Diagnosis for Automata Based on Local Wave Noise Reduction and Bispectral Analysis[J]. Acta Armamentarii, 2014, 35(7): 1077-1082.

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