Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis

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dc.contributor.authorBrahimi, L.
dc.contributor.authorHadroug, N.
dc.contributor.authorIratni, A.
dc.contributor.authorHafaifa, A.
dc.contributor.authorColak, I.
dc.date.accessioned2024-05-19T14:33:16Z
dc.date.available2024-05-19T14:33:16Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractGas turbine malfunctions can significantly impact production and safety. This study proposes an intelligent monitoring system for MS5002C gas turbines using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Long Short-Term Memory (LSTM) algorithms for real-time anomaly detection and predictive maintenance. Based on extensive historical data (1985–2021), the system predicts component degradation and calculates failure probabilities. This enables the development of an effective preventive maintenance plan, extending turbine life and optimizing performance. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.cie.2024.110094
dc.identifier.issn0360-8352
dc.identifier.scopus2-s2.0-85189662363en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cie.2024.110094
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4164
dc.identifier.volume191en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers and Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAdaptive Neuro-Fuzzy İnference Systems (Anfıs)en_US
dc.subjectGas Turbineen_US
dc.subjectIntelligent Real-Time Monitoringen_US
dc.subjectLong Short-Term Memory (Lstm) Algorithmsen_US
dc.subjectMaintainabilityen_US
dc.subjectReliability Modelingen_US
dc.titleAdvancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysisen_US
dc.typeArticleen_US

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