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

• Main Author: Liao Li
• Format: Article
• Language: English
• Published: Departamento de Ciência e Tecnologia Aeroespacial 2021-10-01
• Series: Journal of Aerospace Technology and Management
• Subjects: aircraft engine; fault diagnosis; random forest; particle swarm optimization-back-propagation; air flow; fuel-air ratio
• Online Access: https://www.scielo.br/j/jatm/i/2021.v13/

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.