Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis

Abstract

Vibration signals of gearboxes working under time-varying conditions are non-stationary, which causes difficulties to the fault diagnosis. Based on the techniques of signal sparse decomposition and order tracking, a novel method is proposed to extract fault features from non-stationary vibration signals of gearboxes. The method contains two key procedures, the quasi-steady component separation in angle domain and the impact resonance component extraction in time domain. The sparse dictionary including quasi-steady sub-dictionary and impact sub-dictionary is specifically designed according to the time-frequency characteristics of steady-type fault and impact-type fault. The former sub-dictionary consists of cosine functions and is based on the order spectrum information of angle domain signal. The latter sub-dictionary consists of the unit impulse response of multiple-degree-of-freedom vibration system whose modal parameters are self-adaptively recognized by the method of correlation filtering. An improved matching pursuit algorithm on segmental signal is designed to solve sparse coefficients and reconstruct steady-type fault components and impact-type fault components. The simulation analyses show that the proposed method is capable to process the signal with 30% speed fluctuation and −1.5 dB signal-to-noise ratio (SNR), in which the SNR of impact-type fault components is as low as −14.6 dB. The effectiveness is further verified by experimental tests on a fixed-shaft gearbox and a planetary gearbox.