A Learning Variable Neighborhood Search Approach for Induction Machines Bearing Failures Detection and Diagnosis

Charaf Eddine Khamoudj, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Mohamed Benbouzid and Abdenaser Djaafri
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Charaf Eddine Khamoudj: Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole Nationale Supérieure d’Informatique (ESI), 16270 Alger, Algeria
Fatima Benbouzid-Si Tayeb: Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole Nationale Supérieure d’Informatique (ESI), 16270 Alger, Algeria
Karima Benatchba: Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole Nationale Supérieure d’Informatique (ESI), 16270 Alger, Algeria
Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Abdenaser Djaafri: Computer Science Department, University of Guelma, 24000 Guelma, Algeria

Energies, 2020, vol. 13, issue 11, 1-30

Abstract: This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process. The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach.

Keywords: induction machine; bearing failure; variable neighborhood search (VNS); classical mechanics-inspired optimization (CMO); clustering; failure detection; failure diagnosis; learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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