Surge Fault Detection of Aeroengines Based on Fusion Neural Network

Abstract

Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor speed (N2), power level angle (PLA), etc. Secondly, aiming at preprocessing of data sets, this paper proposed a data standardization preprocessing algorithm based on batch sliding window (DSPABSW) to build a training set, validation set and test set. Thirdly, aeroengine surge fault detection fusion neural network (ASFDFNN) was provided and optimized to improve the detection accuracy of aeroengine surge faults. Finally, the experimental results showed that the model achieved 95.7%, 93.6% and 94.7% in precision rate, recall rate and F1_Score respectively and consequently it can detect the aeroengine surge fault 260 ms in advance.