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The squared envelope spectrum (SES) is one of the most effective methods in rolling element bearing fault diagnosis. Being a technique based on demodulation, the success of SES depends highly on the extraction of information caused by the bearings fault. Therefore, a preprocessing step of choosing an optimal frequency band (OFB), before the implementation of the SES, is absolutely needed and should always be taken prior. Fast kurtogram (FK), as the most commonly used method targeting resonance frequency band which is excited by the bearing fault, is applied as the benchmark tool for the selection of the OFB. However, recent theoretical works show that the kurtosis used as the criterion for OFB selection was both sensitive to the impulsiveness and cyclostationarity, which means irrelevant impulsive and cyclostationary components are able to mislead the kurtosis based OFB selection techniques and subsequently bring great difficulties in bearing fault diagnosis. To tackle this problem, in this paper, a novel statistical model parameter-based method, called Distcsgram, is proposed as a substitution to the traditional FK technique so that an OFB can be selected with the least influences from the irrelevant cyclostationary and impulsive interferences. Together with the SES, the Distcsgram is explained and validated in both simulation and experimental studies. Furthermore, through the comparisons with the traditional methods, the superiority of the proposed method over those traditional methods for the diagnosis of rolling element bearing is proved.

Rolling Element Bearings Fault Diagnosis Based on a Novel Optimal Frequency Band Selection Scheme