Aero Engine Fault Diagnosis Based on Support Vector Machine

In order to improve the accuracy and efficiency of aero-engine fault diagnosis, support vector machine (SVM) technology is used in this study. SVM as a powerful supervised learning model, performs well in classification and regression problems, especially for dealing with high-dimensional data and complex nonlinear relationships. In this paper, the process of data acquisition and preprocessing is described first, and then the influence of different kernel functions on the model performance is discussed in the process of SVM model construction, and the optimal model parameters are determined by cross-validation and other methods. In the part of experiment setting, the key information such as experimental environment, data set division and performance evaluation index are explained in detail. Experimental results show that compared with traditional methods, the SVM based aero engine fault diagnosis method has significantly improved the fault identification accuracy and diagnosis speed.