OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS
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| Format: | Artículo científico |
| Language: | en |
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Universidad Nacional de Colombia
2013
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| author | OSCAR CARDONA MORALES |
| author_facet | OSCAR CARDONA MORALES |
| contents | OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS OSCAR CARDONA MORALES DIEGO A. ÁLVAREZ MARÍN GERMAN CASTELLANOS-DOMINGUEZ Ingeniería One Dynamic features Data description class classification 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. 2013 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49629318022 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.182 Vol.80 |
| format | Artículo científico |
| id | redalyc_49629318022 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS OSCAR CARDONA MORALES Ingeniería One Dynamic features Data description class classification OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS OSCAR CARDONA MORALES DIEGO A. ÁLVAREZ MARÍN GERMAN CASTELLANOS-DOMINGUEZ Ingeniería One Dynamic features Data description class classification 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. 2013 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49629318022 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.182 Vol.80 |
| title | OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS |
| topic | Ingeniería One Dynamic features Data description class classification |
| url | https://www.redalyc.org/articulo.oa?id=49629318022 |