An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

Abstract

This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

“…The integration of new artificial intelligence methodologies and PHM frameworks has been one of the main goals in aircraft engine health management, as it can help to better describe and predict the complex nature of aero-engine faults and deterioration. Several recent works have proposed the joint operation of typical PHM methodologies with other stages, such as fault severity estimation, deterioration prognostics, and innovative remaining useful life estimation approaches [15][16][17][18]. However, due to the complexity of combining different algorithms to efficiently interact with each other and the creation of multiple system configurations, it is necessary to continue to develop and improve such unified solutions by taking advantage of progress in machine learning and deep learning.…”