Sparse signal decomposition method based on the dual Q-factor and its application to rolling bearing early fault diagnosis

Non-stationary signals are a mixture of sustained oscillations and non-oscillatory transients that are difficult to analyze by linear methods. Aiming at this problem, a nonlinear signal analysis method based on Q-factor is proposed, which expresses the non-stationary signal as the sum of a high-resonance (high Q-factor) and a low-resonance component (low Q-factor). And then the dual Q-factor is used to make the signal be sparse-decomposed and the high-resonance component and low-resonance component of the signal are obtained. Applying this method to bearing early fault diagnosis, fault signals are made of high-resonance components and impulsion fault signals (low-resonance component). And impulsion fault signals with strong background noise are extracted by low Q-factor and early impact characteristics of weak damage signals of the bearing are extracted successfully and quickly. The analysis results of simulating data and experimental data show that the proposed method has good denoising effect and it could remove the strong background noise effectively. © 2013 Journal of Mechanical Engineering.