Cm Classifier

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Mechanical Systems and Signal Processing Mech anica l Syste ms and Signal Processin g 21 (2007) 840–855 Condition monitoring and classication of rotating machinery using wavelets and hidden Markov models Qiang Miao a , Viliam Makis b, a School of Mecha tronics Engine ering , Unive rsity of Electr onic Science and Techn ology of China , Chengd u, Sichua n 610054, China b Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ont., Canada M5S 3G8 Received 14 September 2005; received in revised form 14 January 2006; accepted 23 January 2006 Available online 20 March 2006 Abstract Condit ion monitoring and class icatio n of machinery state is of great practical signican ce in manufa cturin g indus try, because it provides updated inf ormation regarding mach ine status on- line, thus avoiding the pro duct ion loss and minimising the chances of catastrophic machine failure. In this paper, the condition classication is based on hidden Markov model s (HMMs) processi ng informat ion obtained fr om vi brat ion signals. We present an on-line faul t classication system with an adaptive model re-estimation algorithm. The machinery condition is identied by selecting the HMM which maximi ses the proba bility of a given observati on sequence. The proper selection of the observ ation sequence is a key step in the development of an HMM-based classication system. In this paper, the classication system is validated using observation sequences based on the wavelet modulus maxima distribution obtained from real vibration signals, which has been proved to be effective in fault detection in previous research. r 2006 Elsevier Ltd. All rights reserved. Keywords:  Co ndit ion moni toring; Rotat ing machi nery; Wavel et modu lus maxima distr ibuti on; Lips chitz expon ent; Cond ition classication; Hidden Markov model (HMM) 1. Introdu ction In recent years, the rapid development of industry automation has motivated the need of more intelligent and reliable machini ng systems. To minimise the loss due to the interruption of production and high machine failure cost, it is necessary to monitor machine condition on-line using an effective condition monitoring syst em to provide timely informatio n for mainte nance decis ion-ma king. Generally, condi tion monit oring involves the observation of machine condition using periodically sampled dynamic response measurements such as vibration signals obtained from several transducers. Vibration measurements obtained from the machine usually contain a lot of useful information, but also noise components which should be eli mi nated fr om the si gn al before the informat ion is us ed for the classication of machine condition and maintenance planning. The success of a classication system depends AR TIC LE IN PR ESS www.elsevier.com/locate/jnlabr/ymssp 0888-3270/$- see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.ymssp.2006.01.009 Corresponding author. E-mail addresses:  [email protected] (Q. Miao), [email protected] (V. Makis).

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