Gas Turbine Engine Health Monitoring and Fault Diagnosis
Summary
Gas turbine engines underpin modern aviation and power generation, yet their complex thermodynamic and mechanical systems are prone to gradual degradation and abrupt faults. Health monitoring and fault diagnosis have evolved from periodic manual inspections to continuous online assessment frameworks that fuse physical modelling with data analytics. By tracking parameters such as exhaust gas temperature, pressure ratios and vibration signatures, these systems quantify component wear, detect anomalies and predict remaining useful life. Modern strategies combine physics-based gas-path analysis with machine-learning algorithms, enabling both interpretable diagnostics and adaptive learning from operational data. Such integrated approaches support condition-based maintenance, improve engine reliability, reduce unscheduled downtime and contribute to environmental and economic sustainability.
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Gas Turbine Engine Health Monitoring and Fault Diagnosis publication trend
The graph below shows the total number of articles in gas turbine engine health monitoring and fault diagnosis across all publications each year (not limited to Nature Index journals).
Technical terms
Condition-based maintenance (CBM): Maintenance strategy driven by continuous monitoring of equipment health rather than fixed schedules.
Gas-path analysis (GPA): Physics-based modelling of airflow and thermodynamic parameters to detect and quantify component degradation.
Data-driven analytics: Use of statistical and machine-learning techniques to infer engine health and predict faults from operational data.
Convolutional neural network (CNN): A deep-learning architecture adept at extracting spatial and temporal patterns for fault detection and classification.
References
- A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics. Applied Energy (2023).
- Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks. Machines (2021).
- A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions. Applied Energy (2022).
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