Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Gas 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 Ltd
Açıklama
Anahtar Kelimeler
Adaptive Neuro-Fuzzy İnference Systems (Anfıs), Gas Turbine, Intelligent Real-Time Monitoring, Long Short-Term Memory (Lstm) Algorithms, Maintainability, Reliability Modeling
Kaynak
Computers and Industrial Engineering
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
191
