Intelligent Performance Degradation Prediction of Light-Duty Gas Turbine Engine Based on Limited Data

• Published in: Symmetry (Basel) Vol. 17; no. 2; p. 277
• Main Authors: Hu, Chunyan, Miao, Keqiang, Zhou, Mingyang, Shen, Yafeng, Sun, Jiaxian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Algorithms; Artificial intelligence; Aviation; Cost control; Datasets; Efficiency; Environmental protection; Fault diagnosis; Gas turbine engines; Gas-turbines; Genetic algorithms; Life prediction; Low pressure; Machine learning; Maintenance costs; Neural networks; Performance degradation; Photodegradation; Real gases; Rotor speed; Simulation; Sustainable development; Thrust; Time series; China
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym17020277

The health monitoring system has been the main technological approach to extending the life of gas turbine engines and reducing maintenance costs resulting from performance degradation caused by asymmetric factors like carbon deposition, damage, or deformation. One of the most critical techniques within the health monitoring system is performance degradation prediction. At present, most research on degradation prediction is carried out using NASA’s open dataset, C-MAPSS, without considering that monitoring measurements are not always available, as in the ideal dataset. This limitation makes fault diagnosis algorithms and remaining useful life prediction methods difficult to apply to real gas turbine engines. Therefore, to solve the problem of performance degradation prediction in light-duty gas turbine engines, a prediction diagram is proposed based on Long Short-Term Memory (LSTM). Various types of onboard signals are taken into consideration among the experimental data. Only accumulated usage time, total temperature and total pressure before the inlet, low-pressure rotor speed, high-pressure rotor speed, fuel flow rate, exhaust temperature, and thrust are used in the training process, which is indispensable for an aero-engine. A genetic algorithm (GA) is introduced into the training process to optimize the hyperparameters of LSTM. The performance degradation prediction modeled with the GA-LSTM method is validated using experimental data. The maximum prediction error of thrust is 70 daN, and the mean absolute percentage error (MAPE) is less than 0.04. This study provides a practical approach to implementing performance degradation prediction in health monitoring systems to improve gas turbine engine reliability, economy, and environmental performance.