Tapered Roller Bearing Failure Diagnosis Based on Improved Probability Box Model

Abstract

Bearing failure often occurs in rotating machinery. The fault diagnosis method based on the vibration signals has been studied for many years. This paper proposed an improved probability box (ip-box) modeling method for diagnosing bearing faults. The major theoretical principles involved with the probability box (p-box) modeling methods and a projection method. Since a larger aggregated width results in the p-box not being conducive to a fault identification and diagnosis, the mean of the focal element interval and the amount of data fluctuation between the adjacent focal elements were used as additional information. Then, the additional information was added to the ip-box model by the cooperative optimization method. Finally, the experimental results showed that the classification performance of a support vector machine (SVM) trained with eight measured values from the ip-box was significantly improved.