Flight Data Based Condition Monitoring and Fault Diagnosis of Aero-Engine

During flying test life cycle, aircraft engine conditions change greatly and faults have been encountered frequently. There always exist urgent needs about monitoring parameters trending on-line, predicting possible faulty condition and diagnosing the specific type when faulty condition encountered. The problem of condition monitoring and fault diagnosis based on flight test data has been studied in this paper. ANN-NARX parameters predicting model of aero engines has been built based on actual flight test data. Considering large demands on data samples, the complex and large design space of ANN model, consequently long training time and input-output time delaying, the model architectures and minimum sample demands have been optimized based on evolving algorithms. The self-adapting thresholds of predicting model have been set using Monte-Carlo method. The specific fault diagnosis has been realized by constructing parity space residual model. All models in this paper have been tested through flight data and applied in actual flying test. The monitoring model could be built based on limited flights in actual flying test. The maximum relative error of high-pressure spool speed, pressure in compressor outlet, total temperature in low-pressure turbine outlet and temperature of all returned oil is 1.0%, 1.7%, 0.2% and 1.2%, respectively. The model predicting uncertainty could be greatly reduced using adaptive thresholds by considering both modeling error and measurement uncertainty. The ratio of detecting and diagnosing specific faulty type is 95.2% based on test samples, which have been encountered in actual flight condition. © 2021, Editorial Department of Journal of Propulsion Technology. All right reserved.