Intelligent Helicopter Turbine Engine Fault Diagnosis Using Multi-Head Attention - Park, Yong Hun; Oh, Hwan In; Kim, In Tae; Lee, So Jung; Moon, Se Hee; Park, Gyu Jin; Park, Jeong Kyu; Jung, Joon Ha (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, 2024)
A turbine engine provides power to the helicopter, enabling the helicopter to travel and hover in the air. Since the rotorcraft operates at high altitudes, ensuring safety and maintaining a healthy operational status are crucial at all times. Therefore, a prognostics and health management (PHM) system for the turbine engine must be implemented to predict any anomalies or faults to prevent catastrophic accidents. This research proposes a novel fault diagnosis method for helicopter turbine engines based on operational data acquired from actual aircraft. First, the proposed method predicts engine torque using other operational data while accounting for uncertainty. A Bayesian regression approach is employed to predict the engine torque. The torque margin, defined as the difference between the actual torque and the estimated torque, is then used to diagnose engine faults. Specifically, a multi-head attention mechanism is incorporated to capture interactions between various engine parameters. Additionally, domain adaptation techniques are applied to enhance the model's generalization performance, ensuring robustness across diverse operating conditions. The proposed method is validated using seven different datasets, each acquired from a helicopter engine. Four datasets were used for training, while the remaining three were allocated for testing and validation. The results indicated that the proposed method accurately predicted torque. Furthermore, the fault diagnosis showed promising results, leading to a 3rd-place finish in the 2024 PHM Society Data Challenge in terms of validation score.
Keywords: Bayesian regression; Catastrophic accidents; Engine torque; Fault diagnosis method; Faults diagnosis; Health management systems; Operational data; Power; Prognostic and health management; Uncertainty