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<p>As global energy demand rises, modern hydrocarbon and gas transport strategies require significant investments in innovative technologies to ensure safe, efficient, and cost-effective operations. Gas compression and transport stations, which rely on turbines, often face substantial production losses due to malfunctions in rotating components. Addressing these challenges, this study introduces a real-time monitoring system for the Titan 130 turbine, leveraging a hybrid model that integrates long short-term memory (LSTM) networks with an adaptive neuro-fuzzy inference system (ANFIS). This combination enables accurate prediction of turbine variables, facilitating early fault detection and improving system availability. Additionally, the developed monitoring strategy includes an operational assistance system that aligns with maintenance planning preferences. By incorporating automated shutdown triggers and preemptive alarms, the system mitigates the risk of severe damage, thus enhancing overall turbine efficiency and performance.</p>

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Real-Time Gas Turbines Monitoring Using Adaptive Neuro-Fuzzy Prediction for Enhanced Efficiency and Availability

  • 期刊论文

  • 发表日期：

    2025年6月6日

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  • Tarek Idris Bisker,
  • Nadji Hadroug,
  • Ahmed Hafaifa,
  • Abdelhamid Iratni,
  • Ahmed Saïd Nouri,
  • Ilhami Colak

摘要

As global energy demand rises, modern hydrocarbon and gas transport strategies require significant investments in innovative technologies to ensure safe, efficient, and cost-effective operations. Gas compression and transport stations, which rely on turbines, often face substantial production losses due to malfunctions in rotating components. Addressing these challenges, this study introduces a real-time monitoring system for the Titan 130 turbine, leveraging a hybrid model that integrates long short-term memory (LSTM) networks with an adaptive neuro-fuzzy inference system (ANFIS). This combination enables accurate prediction of turbine variables, facilitating early fault detection and improving system availability. Additionally, the developed monitoring strategy includes an operational assistance system that aligns with maintenance planning preferences. By incorporating automated shutdown triggers and preemptive alarms, the system mitigates the risk of severe damage, thus enhancing overall turbine efficiency and performance.