New Method for Improving Tracking Accuracy of Aero-Engine On-Board Model Based on Separability Index and Reverse Searching

Throughout its service life, an aero-engine will experience a series of health conditions due to the inevitable performance degradation of its major components, and characteristics will deviate from their initial states. For improving tracking accuracy of the self-tunning on-board engine model on the engine output variables throughout the engine service life, a new method based on the separability index and reverse search algorithm was proposed in this paper. By using this method, a qualified training set of neural networks was created on the basis of eSTORM (enhanced Self Tuning On-board Real-time Model) database, and the problem that the accuracy of neural networks is reduced or even that the training process is not convergent can be solved. Compared with the method of introducing sample memory factors, the method proposed in this paper makes the self-tunning on-board model maintain higher tracking accuracy in the whole engine life, and the algorithm is simple enough for implementation. Finally, the training set center generated in the calculation process of the proposed method could be used for the real-time monitoring of the engine gas path parameters without additional calculations. Compared with the commonly used sliding window method, the proposed method avoids the problem of low algorithm efficiency caused by fewer abnormal data samples.