Abstract
Gearbox fault data are usually characterized by nonstationarity and multiple scaling behaviors, whose underlying dynamical mechanism detrended fluctuation analysis (DFA) often fails to uncover. Multifractal DFA (MF-DFA) is an extension of DFA and able to effectively reveal the underlying dynamical mechanism hiding in nonstationary data with multiple scaling behaviors. To start with, MF-DFA was used to computer the multifractal singularity spectrum of gearbox fault data. Next, the four characteristic parameters, including the multifractal spectrum width, the maximum singularity exponent, the minimum singularity exponent and the singularity exponent corresponding to the extremum of the multifractal spectrum, bear clear physical meaning, express the underlying dynamical mechanism of gearbox fault data and can be employed as fault features of gearbox fault data. Consequently, a novel method for feature extraction of gearbox fault data was proposed based on MF-DFA. Besides, the proposed method together with DFA was utilized to separate the normal, the slight-worn, the medium-worn and the broken-tooth vibration data from a four-speed motorcycle gearbox. The results show that the proposed method, overcoming the deficiencies of DFA, is sensitive to small changes of gearbox fault conditions, can totally separate the similar and close fault patters and seems to be a feasible method for feature extraction of gearbox fault data.
Key words
multifractal /
detrended fluctuation analysis /
gearbox /
feature extraction
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LIN Jin-shan;CHEN Qian .
Fault Feature Extraction of Gearboxes Based on Multifractal Detrended Fluctuation Analysis[J]. Journal of Vibration and Shock, 2013, 32(2): 97-101
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