Abstract

CARDONA MORALES, OSCAR; ALVAREZ MARIN, DIEGO A.  and  CASTELLANOS-DOMINGUEZ, GERMAN. OUTLIER DETECTION IN ROTATING MACHINERY UNDER NON-STATIONARY OPERATING CONDITIONS USING DYNAMIC FEATURES AND ONE-CLASS CLASSIFIERS. Dyna rev.fac.nac.minas [online]. 2013, vol.80, n.182, pp.173-181. ISSN 0012-7353.

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.

Keywords : Dynamic features; One-class classification; Data description.

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