A new statistical modeling and detection method for rolling element bearing faults based on alpha–stable distribution

Abstract

Due to limited information given by traditional local statistics, a new statistical modeling method for rolling element bearing fault signals is proposed based on alpha–stable distribution. In order to fully take advantages of complete information provided by alpha–stable distribution, this paper focuses on testing the validity of the proposed statistical model. A number of hypothetical test methods were applied to practical bearing fault vibration signals with different fault types and degrees. Through testing on the consistency of three alpha–stable parameter estimation methods, and the probability density function fitting level between fault signals and their corresponding hypothetical alpha–stable distributions, it can be concluded that such a non-Gaussian model is sufficient to thoroughly describe the statistical characteristics of bearing fault signals with impulsive behaviors, and consequently the alpha–stable hypothesis is verified. In the meantime, a new bearing fault detection method based on kurtogram and α parameter of the alpha–stable model is proposed, experimental results have shown that the proposed method has better performance on detecting incipient bearing faults than that based on the traditional kurtogram.