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

Autor: Vepa Atamuradov, Benjamin Lamoureux, Noureddine Zerhouni, Fatih Camci, Pierre Dersin, Kamal Medjaher
Přispěvatelé: Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Nationale Supérieure de Mécanique et des Microtechniques - ENSMM (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université de Franche-Comté (FRANCE), Université de Technologie de Belfort-Montbéliard - UTBM (FRANCE), ALSTOM Transport (FRANCE), Amazon Inc. (USA), Assystem (FRANCE), Université Bourgogne Franche-Comté - UBFC (FRANCE), Laboratoire Génie de Production - LGP (Tarbes, France), Institut Franche-Comté Electronique Mécanique Thermique et Optique - Sciences et Technologies - FEMTO-ST (Besançon, France), Assystem France, Laboratoire Génie de Production (LGP), Ecole Nationale d'Ingénieurs de Tarbes, Alstom Transport (Alstom Transport), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Alstom Transport (FRANCE)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Quality and Reliability Engineering International
Quality and Reliability Engineering International, Wiley, 2019, pp.1-19. ⟨10.1002/qre.2446⟩
ISSN: 0748-8017
1099-1638
DOI: 10.1002/qre.2446⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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