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Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Atamuradov, Vepa and Medjaher, Kamal and Camci, Fatih and Zerhouni, Noureddine and Dersin, Pierre and Lamoureux, Benjamin Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation. (2019) Quality and Reliability Engineering International. 1-19. ISSN 0748-8017

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Official URL: https://doi.org/10.1002/qre.2446

Abstract

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.

Item Type:Article
HAL Id:hal-02053270
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Ecole Nationale Supérieure de Mécanique et des Microtechniques - ENSMM (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Université de Franche-Comté (FRANCE)
Other partners > Université de Technologie de Belfort-Montbéliard - UTBM (FRANCE)
Other partners > ALSTOM Transport (FRANCE)
Other partners > Amazon Inc. (USA)
Other partners > Assystem (FRANCE)
Other partners > Université Bourgogne Franche-Comté - UBFC (FRANCE)
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Deposited On:13 Feb 2019 10:52

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