Coupled hidden Markov fusion of multichannel fast spectral coherence features for intelligent fault diagnosis of rolling element bearings

Comprehensive understanding for machinery health monitoring could be realized by multisource information. Nevertheless, effective fusion of faulty features extracted from multichannel sensors remains challenging in intelligent fault diagnostics. For this reason, coupled hidden Markov model (CHMM) is proposed in this work to efficiently fuse spectral coherence (SC) features extracted from multichannel sensors for improving the diagnosis performance of rolling element bearings (REBs). To this end, SC features extracted from multichannel vibration data of the REB under different operational conditions are fused by a multichain CHMM. Parameter estimation algorithms and probabilistic inference for the CHMM are also developed in the addressed fusion approach. The effectiveness of the proposed method is validated by two different diagnosis experiments, one for fault classification and another for lifecycle performance evaluation of REBs. Compared with the state-of-the-art peer methods, the proposed multichannel feature fusion method has the best performance when dealing with fault diagnosis tasks for REBs.