Reactor fault diagnosis based on common feature of multivariate vibration sequences

Aiming at the limitation that current feature selection algorithms are only available for univariate vibration sequence, this paper proposes a multivariate vibration sequences feature selection algorithm named SVM-RFE-GA based on support vector machine recursive feature elimination algorithm (SVM-RFE) and genetic algorithm (GA). Taking a 220 kV high voltage shunt reactor as the research object, we build a mechanical fault simulation platform, set up 5 kinds of equipment states and collect multivariate vibration sequences of different equipment states at 24 sampling positions on its surface. We construct the feature pool from the time domain, frequency domain and time-frequency domain. For single vibration sequences, we rank the features and select features preliminarily by SVM-RFE. Then, the preliminarily select features are further optimized by GA algorithm to select the feature combination with the highest accuracy and the least number. The experimental result shows that the proposed method can select the common feature combination of multivariate vibration sequences, and the combination can ensure the highest fault diagnosis accuracy and the minimum number of features.