Brahimi, L.Hadroug, N.Iratni, A.Hafaifa, A.Colak, I.2024-05-192024-05-1920240360-8352https://doi.org/10.1016/j.cie.2024.110094https://hdl.handle.net/20.500.12713/4164Gas 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 Ltdeninfo:eu-repo/semantics/closedAccessAdaptive Neuro-Fuzzy İnference Systems (Anfıs)Gas TurbineIntelligent Real-Time MonitoringLong Short-Term Memory (Lstm) AlgorithmsMaintainabilityReliability ModelingArticle1912-s2.0-8518966236310.1016/j.cie.2024.110094Q1