Fault diagnosis for a turbine engine

Fault detection and diagnosis for jet engines is complicated by the presence of engine-to-engine manufacturing differences and engine deterioration during normal operation, the complexity of an accurate engine model, and our inability to directly measure certain engine variables. Here, we work with a sophisticated component level model (CLM) simulation of a turbine engine (the General Electric XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of faults. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by a Levenberg–Marquardt method to train a Takagi–Sugeno fuzzy system to represent the engine. The resulting nonlinear model provides a reasonably accurate representation of manufacturing differences, engine deterioration, and fault effects. We use multiple copies of this nonlinear model, each representing a different fault, to generate error residuals by comparing them to the engine output. In fact, we manage the composition of the set of models with a ‘‘supervisor’’ that ensures that the appropriate models are on-line, and processes the error residuals to detect and identify faults. Robustness and fault sensitivity of the proposed approach are studied in the paper and the component model level simulation of the XTE46 engine is used to illustrate the effectiveness of the fault diagnosis scheme. r 2004 Elsevier Ltd. All rights reserved.