文档介绍：ARTICLE IN PRESS
Mechanical Systems
and
Signal Processing
Mechanical Systems and Signal Processing 18 (2004) 1077–1095
ate/jnlabr/ymssp
Bearing fault diagnosis based on wavelet transform
and fuzzy inference
Xinsheng Lou1, h A. Loparo*
Department of Electrical puter Science, Case Western Reserve University, 10900 Euclid Avenue,
Cleveland, OH 44106, USA
Received 12 May 2003; received in revised form 19 May 2003; accepted 22 May 2003
Abstract
This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the
wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner
race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform
was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy
inference system (ANFIS) was trained and used as a diagnostic classifier. parison purposes, the
Euclidean vector distance method as well as the vector correlation coefficient method were also
investigated. The results demonstrate that the developed diagnostic method can reliably separate different
fault conditions under the presence of load variations.
r 2003 Elsevier Ltd. All rights reserved.
Keywords: Wavelets; Fault diagnosis; Fuzzy inference; Pattern classification; Bearings
1. Introduction
Condition monitoring of rotating machinery is important in terms of system maintenance and
process automation. Rolling element bearing failures are one of the foremost causes of failures in
rotating machinery. This necessitates the development, implementation, and deployment of on-
line diagnostic monitoring systems that are independent of operating conditions.
In most machine fault diagnosis and prognosis systems, the vibration of the rotating machine
(motor, gearbox, etc.) is directly measured by an accelerometer, in some few cases, by an acoustic
pickup. Some techniques use the stator