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ARTICLE IN PRESS Signal Processing www elsevier com locate sigpro An online support vector machine for abnormal events detection Manuel Davya 1 Fre de ric Desobryb 1 Arthur Grettonc 1 Christian Doncarlid a Laboratoire d Automatique Ge nie Informatique et Signal UMR CNRS 8146 Ecole Centrale de Lille BP 48 59651 Villeneuve d Ascq cedex France b Signal Processing group Department of Engineering University of Cambridge Trumpington Street Cambridge CB2 1PZ UK c Max Planck Institut fu r biologische Kybernetik Tuebingen Germany d Institut de Recherche en Cyberne tique de Nantes UMR CNRS 6597 1 rue de la Noe BP 92101 44321 Nantes Cedex 3 France Received 13 March 2003 received in revised form 24 March 2005 accepted 27 September 2005 Abstract The ability to detect online abnormal events in signals is essential in many real world signal processing applications Previous algorithms require an explicit signal statistical model and interpret abnormal events as statistical model abrupt changes Corresponding implementation relies on maximum likelihood or on Bayes estimation theory with generally excellent performance However there are numerous cases where a robust and tractable model cannot be obtained and model free approaches need to be considered In this paper we investigate a machine learning descriptor based approach that does not require an explicit descriptors statistical model based on support vector novelty detection A sequential optimization algorithm is introduced Theoretical considerations as well as simulations on real signals demonstrate its practical ef ciency r 2005 Elsevier B V All rights reserved Keywords Abnormality detection Support vector machines Sequential optimization Gearbox fault detection Audio thump detection 1 Introduction Online anomaly detection in signals or systems is a general framework which includes many specialized applications such as industrial monitoring motor fault detection 1 2 gas turbine monitoring 3 etc or audio restoration 4 Among the



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