Synergistic use of soft computing technologies for fault detection in gas turbine engines

In this paper, we present a synergistic approach to startup fault detection and diagnosis in gas turbine engines. The method employs statistics, signal processing and soft computing techniques in a complementary manner to address fault detection at transient conditions. Traditional turbine engine fault detection and diagnosis methods are based on engine data collected at steady-state conditions. However, insipient faults are difficult to diagnose using steady-state engine date; only engine faults that are fairly developed can be detected using conventional methods. Because incipient engine component faults are often manifested in the engine startup characteristics, we present a method of characterizing the engine transient startup using the following steps: engine sensor data during startup are recorded in time series format. The sensor profiles corresponding to "good" and "bad" engine startups are sampled using the bootstrap technique. A feature vector is extracted from these data using principal component analysis (PCA). Then, several important discriminating features are distilled from the feature vector. The features obtained from this step are then classified using neural-network-based methods. The "leave-one-out" approach to cross-validation is applied to obtain an objective evaluation of the neural network training. The proposed fault detection and diagnosis method is evaluated using actual engine startup data and the results are presented.