Engine Testing Fault Classification Based on the Multi-class SVM of Auto-regression

Abstract

In connection with the problem of lack of an effective method for vibration fault diagnose of aero-engine during the testing process in test cell, this paper proposed a classification method for aero-engine’s different types of fault modes based on a time series autoregressive (AR) model and support vector machine (SVM) classifier. First, respectively collect 200 kinds of vibration signals in normal state and three kinds of fault state from the engine test cell. Establish AR model for the training set of aero-engine vibration signals through the autocorrelation algorithm, then obtained by the feature vectors consist of autoregressive parameters and residual variance. Then create SVM classifier, the obtained vibration signal feature vector will be entered into the SVM classifier, adjusting the penalty parameter c and the kernel function parameter g through the optimization algorithm, the ideal forecasting classification model is available. Finally, conduct classification identification on fault types of different test sets through the obtained classification models. Experimental results verified this method was effective to engine’s classification for different vibration fault modes under the conditions of small samples and had high classification accuracy.