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Outlier detection in rotating machinery under non-stationary operating conditions using dynamic features and one-class classifiers

Cardona Morales, Oscar and Álvarez Marín, Diego Andrés and Castellanos Domínguez, Germán (2013) Outlier detection in rotating machinery under non-stationary operating conditions using dynamic features and one-class classifiers. Dyna; Vol. 80, núm. 182 (2013); 173-181 DYNA; Vol. 80, núm. 182 (2013); 173-181 2346-2183 0012-7353 .

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The main goal of condition-based maintenance is to describe the machine state under current operating regimes, which can be non-stationary depending of load/speed changes. Besides, damaged machine data are not always available in real-world applications. This paper proposes a methodology of outlier detection in time-varying mechanical systems based on dynamic features and data description classifiers. Dynamic features set is formed by spectral sub-band centroids and linear frequency cepstral coefficients extracted from time-frequency representations. One-class classification is carried out to validate performance of the dynamic features as descriptors of machine behavior. The methodology is tested with a data set coming from a test-rig including different machine states with variable speed conditions. The proposed approach is validated on real recordings acquired from a ship driveline. The results outperform other time-frequency features in terms of classification performance. The methodology is robust to minimal changes in the machine state and/or time-varying operational conditions.

Tipo de documento:Artículo - Article
Información adicional:Derechos de autor reservados
Palabras clave:Dynamic features, One-class classification, Data description
Unidad administrativa:Revistas electrónicas UN > Dyna
Código ID:31091
Enviado por : Dirección Nacional de Bibliotecas STECNICO
Enviado el día :30 Junio 2014 15:33
Ultima modificación:06 Junio 2018 10:59
Ultima modificación:06 Junio 2018 10:59
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