Running State Evaluation Method of Turbine Bearing Based on Feature Vector

Abstract

Due to the high modal coupling of the cooling turbine bearing in environment control system, it is very difficult to extract the vibration signal feature and construct the recognition model on different feature.Arunning state evaluation method of the turbine bearing is proposed based on the feature vector with limited testing data in this paper. Firstly, aiming at some failure modes in several typical faults of turbine bearing, three time domain feature  parameters and seven frequency domain feature parameters are chosen to construct feature vector for discrimination. Then, the feature vectors of different fault testing data are dimensional reduced based on the principal component analysis method. Based on above, the support vector machine (SVM) model of the turbine bearing running state is proposed for monitoring and predicting the occurrence and development of typical turbine bearing failure modes. Experimental results suggest that the bearing running state evaluation method proposed in this paper can improve the prediction accuracy effectively.