New Cointegration-Based Feature Extraction Technique for Intelligent Bearing Fault Detection Under Time-Varying Speed.

An effective feature vector generation approach is herein presented based on the cointegration concept and the signal processing methods in order to improve varying speed bearing troubleshooting. Initially, each vibration signal is decomposed into its intrinsic mode functions (IMFs) using ensemble empirical mode decomposition method. Then, the cointegration relations are extracted by applying the Johansen trace test method to the obtained components. In order to form the feature matrix, the cointegration relations are analyzed by wavelet packet transform and the time-domain features are calculated for the wavelet packet coefficients. Consequently, by using a hybrid feature selection approach based on compensation distance evaluation technique, support vector machine, and binary particle swarm optimization algorithm, the optimal hybrid features were identified. The experimental results show that the optimal features are composed of the first cointegration relation, the third level of wavelet packet coefficients, the third IMF, Teager–Kaiser energy, energy, and root mean square. Also, the number of optimal features obtained from the first cointegration relation was less than other relationships. The results reveal that when the optimal feature set is computed for the first cointegration relationship, the identification accuracy of the proposed approach increases considerably. The result analysis also shows the efficiency of the proposed technique in detecting the condition of the bearing in time-varying rotational speed. [ABSTRACT FROM AUTHOR]