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.

Research Highlights
► A sequential intelligent diagnosis method for bearing faults is proposed by possibility theory and a fuzzy neural network.
► Non-dimensional symptom parameters are defined in the frequency domain with vibration signals.
► Possibility theory and the Mycin certainty factor are used to process the ambiguous relationship between symptoms and fault types.
► Fault types can be identified sequentially and automatically.