Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network

This paper presents an intelligent diagnosis method for a rolling element bearing; the method is constructed on the basis of possibility theory and a fuzzy neural network with frequency‐domain features of vibration signals. A sequential diagnosis technique is also proposed through which the fuzzy neural network realized by the partially‐linearized neural network (PNN) can sequentially identify fault types. Possibility theory and the Mycin certainty factor are used to process the ambiguous relationship between symptoms and fault types. Non‐dimensional symptom parameters are also defined in the frequency domain, which can reflect the characteristics of vibration signals. The PNN can sequentially and automatically distinguish fault types for a rolling bearing with high accuracy, on the basis of the possibilities of the symptom parameters. Practical examples of diagnosis for a bearing used in a centrifugal blower are given to show that bearing faults can be precisely identified by the proposed method.