A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator

The rolling bearings often suffer from compound faults in practice. The concurrence of different faults increases the fault detection difficulty and the decoupling detection of compound faults is attracting considerable attentions. Recent publications report the application of the multiwavelets and empirical mode decomposition (EMD) for compound faults decoupling. However, due to limited adaptability they would induce mode mixing or/and overestimation problems in the signal processing. Particularly, the mode mixing would greatly degrade their performance on compound faults detection. To address this issue, this work presents a new method based on the empirical wavelet transform-duffing oscillator (EWTDO) for compound faults decoupling diagnosis of rolling bearings. The empirical wavelet transform (EWT) is able to extract intrinsic modes of a signal by fully adaptive wavelet basis. Hence, the mode mixing and overestimation can be resolved in decoupling processing and the compound faults can be correctly decomposed into different single faults in the form of empirical modes. Then, each single fault frequency was incorporated into a duffing oscillator to establish its corresponding fault isolator. By directly observing the chaotic motion from the Poincar mapping of the isolator outputs the single faults were identified one by one from the empirical modes. Experimental tests were carried out on a rolling bearing fault tester to examine the efficacy of the proposed EWTDO method on compound faults detection. The analysis results show attractive performance with respect to existing decoupling approaches based on the multiwavelets and EMD. In particular, our proposed method is much more reliable in decoupling the compound faults. Hence, the proposed method has practical importance in compound faults decoupling diagnosis for rolling bears.