JOURNAL OF SOUND AND VIBRATION www.elsevier.com/locate/jsvi Journal of Sound and Vibration 277 (2004) 1005–1024 Machine fault diagnosis through an effective exact wavelet analysis Peter W. Tse a, *, Wen-xian Yang b , H.Y. Tam a a Smart Asset Management Laboratory, Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Tat Chee Ave., Kowloon, Hong Kong, China b Institute of Vibration Engineering, Northwestern Polytechnic University, Xi’an, 710072, Shaanxi, China Received 26 July 2002; accepted 25 September 2003 Abstract Continuous wavelet transforms (CWTs) are widely recognized as effective tools for vibration-based machine fault diagnosis, as CWTs can detect both stationary and transitory signals. However, due to the problemofoverlapping,alargeamountofredundantinformationexistsintheresultsthataregeneratedby CWTs.Theappearanceofoverlappingcansmearthespectralfeaturesandmaketheresultsverydifficultto interpret for machine operators. Misinterpretation of results may lead to false alarms or failures to detect anomaloussignals.Moreover,asconventionalCWTsonlyuseasinglemotherwavelettogeneratedaughter wavelets, the distortion of the original signal in the resultant coefficients is inevitable. Obviously, this will significantlyaffecttheaccuracyinanomaloussignaldetection.Tominimizetheeffectofoverlappingandto enhance the accuracy of fault detection, a novel wavelet transform, which is named as exact wavelet analysis, has been designed for use in vibration-based machine fault diagnosis. The design of exact wavelet analysis is based on genetic algorithms. At each selected time frame, the algorithms will generate an adaptivedaughterwavelettomatchtheinspectedsignalas exactly aspossible.Theoptimizationprocessof exact wavelet analysis is different from other adaptive wavelets as it considers both the optimization of wavelet coefficients and the satisfaction of the admissibility conditions of wavelets. The results obtained from simulated and practical experiments prove that exact wavelet analysis not only minimizes the undesirable effect of overlapping, but also helps operators to detect faults and distinguish the causes of faults. With the help from exact wavelet analysis, sudden shutdowns of production and services due to the fatal breakdown of machines could be avoided. r 2003 Elsevier Ltd. All rights reserved. ARTICLE IN PRESS *Corresponding author. E-mail address: meptse@cityu.edu.hk (P.W. Tse). 0022-460X/$-see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.jsv.2003.09.031