Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 60, Issue 4, Pages 511-518Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2010.12.004
Keywords
Fault diagnosis; Neural network; Possibility theory; Rolling element bearing; Centrifugal blower
Funding
- JSPS [19360074]
- National Natural Science Foundation of China [51075023]
- Fundamental Research Funds for the Central Universities
- Grants-in-Aid for Scientific Research [19360074] Funding Source: KAKEN
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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. (C) 2010 Elsevier Ltd. All rights reserved.
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