A hybrid optimized approaches for ball bearing state prognosis for effective decision making

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dc.authorscopusidİlhami Çolak / 6602990030
dc.authorwosidİlhami Çolak / KGT-0825-2024
dc.contributor.authorEuldji, Riadh
dc.contributor.authorBoumahdi, Mouloud
dc.contributor.authorBachene, Mourad
dc.contributor.authorEuldji, Rafik
dc.contributor.authorÇolak, İlhami
dc.date.accessioned2025-04-16T20:16:38Z
dc.date.available2025-04-16T20:16:38Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractManufacturing Industries (MI) has developed in the past years, by including different types of machines such as the Rotating Machines (RM) to deliver High-Quality Products (HQP), however those machines are prone to failure, which effects on the production process and leading to high economic loses, the purpose behind this study is to present a new strategy that support’s the decision-making to avoid any unexpected breakdowns in the MI. A novel hybrid technique that combines the Adaptive Neuro Fuzzy Inference System (ANFIS), the Hybrid Whale Grey Wolf Optimizer (HWGO), and the state space (SS) approach using the Vibration Condition Monitoring (VCM) signals to determine the Remaining Useful Life (RUL) is introduced. The proposed approach was trained using features selected by the Decision Tree (DT) algorithm. A comparative analysis is conducted against the HWGO with 12 metaheuristic algorithms in terms of optimization. The SS model was applied for forecasting the future RUL values depending on the measured values by the ANFIS-HGWO. The overall results confirm the outperforming of the ANFIS-HWGO-SS compared to different contributions available in the literature using the PRONOSTIA database in terms of performance measure, Therefore, the ANFIS-HWGO-SS approach is a reliable tool for determining the RUL before any unexpected breakdown and further supports the decision-making by offering a vital timeframe for making the proper action before a failure occurs which can be applied for other machine-related tasks to ensure stability by decreasing the failure of the machine, reliability of the RM by providing HQP, security by avoiding accidents in MI, marking a significant step towards enhanced operational efficiency and sustainability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
dc.description.sponsorshipThe authors gratefully acknowledge the Algerian Ministry of Higher Education and Scientific Research, Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria.
dc.identifier.citationEuldji, R., Boumahdi, M., Bachene, M., Euldji, R., & Colak, I. (2024). A hybrid optimized approaches for ball bearing state prognosis for effective decision making. International Journal of Machine Learning and Cybernetics, 1-19.
dc.identifier.doi10.1007/s13042-024-02498-5
dc.identifier.issn18688071
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1007/s13042-024-02498-5
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6073
dc.identifier.wosWOS:001381620600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÇolak, İlhami
dc.institutionauthoridİlhami Çolak / 0000-0002-6405-5938
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofInternational Journal of Machine Learning and Cybernetics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAdaptive Neuro-Fuzzy Inference System (ANFIS)
dc.subjectDecision Tree (DT)
dc.subjectHybrid Whale Grey Wolf Optimizer (HWGO)
dc.subjectState Space (SS)
dc.titleA hybrid optimized approaches for ball bearing state prognosis for effective decision making
dc.typeArticle

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