Simulation of Hierarchical Fault Diagnosis Model for Aircraft Electromechanical Systems Based on Machine Learning Algorithms

With the growth of information technology, artificial intelligence technologies represented by machine learning and pattern recognition are gradually being applied in the field of fault diagnosis. Due the rapid growth of high-tech, the composition and structure of aircraft electromechanical systems have become increasingly complex. Traditional empirical methods have been difficult to solve some new problems that have emerged. Therefore, seeking fast and accurate fault diagnosis technology has become a new trend in the field of aircraft electromechanical fault diagnosis. Traditional manual troubleshooting is time-consuming and labor-intensive, with low diagnostic efficiency and accuracy. In recent years, machine learning has gradually gained popularity due to its efficiency and accuracy in artificial intelligence fault diagnosis. In response to the characteristics of frequent, difficult to eliminate, and time-consuming and labor-intensive maintenance of aircraft electromechanical faults in aircraft maintenance, this paper proposes the application of RMBP neural network to construct an intelligent diagnosis system for aircraft electromechanical faults. The data-driven machine learning analysis method has good engineering results. The results indicate that the intelligent diagnosis system for aircraft electromechanical faults can correctly diagnose the faults of test samples and meet the needs of aircraft electromechanical fault maintenance.