Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model

The conventional signal processing based methods are difficult to achieve satisfactory results for rolling element bearings (REBs)’ weak fault due to the serious influence of interference signal. Intelligent classification technology and the arising popular monitoring technology-performance evaluation assessment (PDA) are the research hotspots of fault diagnosis of REB in recent years, which could resolve the above problem to some extent. Especially the latter could reflect the operating status of the equipment more comprehensively. Effective feature extraction basing on signal processing methods and intelligent algorithm are the two key aspects for the above two technologies which will determine their effectiveness to great extent. Multifractal detrended fluctuation analyses (MDFA) is an effective non-stationary signal processing method which could reveal the multifractality buried in nonlinear and nonstationary vibration signals of REB, and continuous hidden markov model (CHMM) is a mature intelligent algorithm with solid theoretical basis and rich mathematical structure. So a diagnosis method basing on combination of MDFA with CHMM is proposed in the paper, and it could deal with both fault classification and PDA tasks for the diagnosis of REBs. 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 to state-of-the-art peer methods, the proposed method has the best performance when dealing with fault diagnosis tasks for REBs.