Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model

• Neighbourhood component analysis (NCA) is used in dimensionality reduction and feature extraction.
• A multichannel information fusion method based on CHMM is used for bearing fault diagnosis.
• Rolling element bearing accelerated life test is performed to collect vibration data over whole life time.
• The advantages of proposed method over other methods are verified.

Due to the important role rolling element bearings play in rotating machines, condition monitoring and fault diagnosis system should be established to avoid abrupt breakage during operation. Various features from time, frequency and time–frequency domain are usually used for bearing or machinery condition monitoring. In this study, NCA-based feature extraction (FE) approach is proposed to reduce the dimensionality of original feature set and avoid the “curse of dimensionality”. Furthermore, coupled hidden Markov model (CHMM) based on multichannel data acquisition is applied to diagnose bearing or machinery fault. Two case studies are presented to validate the proposed approach both in bearing fault diagnosis and fault severity classification. The experiment results show that the proposed NCA-CHMM can remove redundant information, fuse data from different channels and improve the diagnosis results.