TQWT-based multi-scale dictionary learning for rotating machinery fault diagnosis

It is a challenging problem to extract periodic impulses submerged in the heavy background noise for fault diagnosis of rotating machinery. Thus, in this paper, we propose a novel algorithm named tunable Q-factor wavelet transform(TQWT)-based multi-scale dictionary learning for dealing with this problem. The algorithm exploits TQWT to decompose the measured vibration signal into different scales, and then it adopts K-SVD which can also be replaced with other more efficient dictionary learning algorithm to learn dictionaries at different scales. Once done, it employs a global maximum a posteriori estimator and inverse TQWT to extract feature signal. By comparison with TQWT-denoising and K-SVD-denoising, the proposed algorithm enjoys two main advantages: 1) the dictionaries learnt by our algorithm have the multi-scale characteristic which is essential to deal with non-stationary signal. 2) the dictionaries are learnt from noisy signals itself and thus are adaptive to different types of feature information. Effectiveness of our proposed algorithm is demonstrated by numerical simulation and fault diagnosis of motor bearing.