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

Authors:
Huaqing Wang;Peng Chen
Affiliations:
School of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chaoyang District, Beisanhuan East Road 15, Beijing 100029, China;Department of Environmental Science and Engineering, Faculty of Bioresources Mie University, 1577 Kurimamachiya-cho, Tsu-shi, Mie-ken 514-8507, Japan
Venue:
Computers and Industrial Engineering
Year:
2011

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Abstract

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.