Aero-engine Gas Path Fault Diagnosis Based on Improved Support Vector Machine and Synergetic Neural Network

摘要

In order to distinguish similar failures of the aero-engine gas path fault diagnosis and improve the diagnostic accuracy, a fault diagnosing method based on improved SVM and Synergetic Neural Network was put forward. Firstly, the SVM after being optimized by Genetic Algorithms was used to diagnose and classify faults preliminarily for measured data, and the diagnosis results were analyzed to acquire indistinguishable similar failures, then the Synergetic Neural Network was introduced to distinguish similar failures and further determine the corresponding fault type, finally analyzed this fault model based on actual data. The experimental results show that the aero-engine gas path fault diagnosis method based on improved SVM and Synergetic Neural Network has high diagnostic accuracy and noise immunity.