Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines

Abstract

For the aircraft, the engine is its core component. Once the engine fails, the flight safety will be seriously affected; therefore, it is necessary to diagnose the failure in time. This paper briefly introduced three aircraft engine fault diagnosis algorithms based on support vector machine (SVM), random forest, and particle swarm optimization-back-propagation (PSO-BP) and carried out a simulation experiment on the performance of the three algorithms in MATLAB software. The results showed that the PSO-BPbased diagnosis algorithm had the highest recognition accuracy and the SVM-based diagnosis algorithm had the lowest, both for artificial fault data and real fault data. The PSO-BP-based diagnosis algorithm took the least average recognition time, and the SVM-based diagnosis algorithm took the longest time.

Keywords

• aircraft engine
• fault diagnosis
• random forest
• particle swarm optimization-back-propagation
• air flow
• fuel-air ratio