Open AccessApplication of resonance demodulation method based on local characteristic-scale decomposition and spectral kurtosis in gearbox fault
Author(s) -
Rui Cong,
C. H. Li,
Jizhong Wu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/657/1/012050
Subject(s) - kurtosis , hilbert–huang transform , demodulation , computer science , fault (geology) , pattern recognition (psychology) , entropy (arrow of time) , artificial intelligence , filter (signal processing) , algorithm , mathematics , computer vision , statistics , telecommunications , physics , channel (broadcasting) , quantum mechanics , seismology , geology
Resonance demodulation technology has been widely used for its advantages on magnification and selection of fault information. However, the selection of filter parameters is affected by the subjectivity, and the analysis results are easily affected by the noisy signals under actual working conditions. To solve this problem, a method combining relative entropy and correlation coefficient is proposed to analyze and select Intrinsic Scale Components obtained by Local Characteristic-scale Decomposition, remove false components, and to some extent, denoise. Then, after reconstructing the remaining components, we use spectral kurtosis analysis to find out the most easily detected frequency bands of fault features and analyze them by resonance demodulation, aiming to diagnose gearbox failures. Finally, we compare this method with that based on the empirical mode decomposition. The research shows that the method proposed in this paper can effectively diagnose the gearbox fault and is better than the empirical mode decomposition.