Operating condition and fault diagnosis of electric submersible pump based on OCSVM

Key words:  electric submersible pump  one-class support vector machine(OCSVM)  feature extraction  operating condition and fault diagnosis

Abstract:

One-class support vector machine(SVM) model was used to distinguish electric submersible pump normal operation and abnormal operating states. Based on only the data in the electric submersible pump normal state, the one-class SVM model is applied to identify abnormal state data. Firstly, we preprocess the electric submersible pump current data and filter the current data under normal conditions. Then, according to the characteristics of the electric submersible pump and data characteristics, six relevant data features are extracted. The one-class SVM model is subsequently used to identify abnormal states including unknown faults, so as to realize the working conditions and fault diagnosis of the electric submersible pump. Finally, the actual production data is used to verify the model. The results prove that the method proposed in this paper has a high recognition accuracy and a strong model generalization ability. Through real-time analysis of daily operation data of the electric submersible pump, the real-time monitoring of status and early warning of abnormal working conditions of the electric submersible pump is realized.