An Aero-engine Gas Path Fault Diagnosis Method Based on OPABC-BP

The gas path fault is the main cause of the aeroengine fault. Aiming at the problems of low diagnostic accuracy and large errors in traditional fault diagnosis methods, this paper designs an aero-engine gas path fault diagnosis method based on the Optimized Artificial Bee Colony-Back Propagation Neural Network (OPABC-BP) algorithm. This method integrates a variety of gas path parameters changes to solve problems such as difficult diagnosis of aero-engine gas path fault and unsatisfied diagnostic accuracy rate. The simulation results show that the diagnostic accuracy rate of the OPABC-BP model proposed in this paper is about 9.28% higher than that of the basic Back Propagation Neural Network (BP) algorithm, and about 4.83% higher than other optimization algorithms. Finally, the method proposed in this paper(OPABC-BP) has a better fault diagnosis effect, which can lay a foundation for the realization of fault prediction and health management.