Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model

Abstract

The importance of induction motor (IM) drives in industrial applications, especially in the renewable energy sector is unarguable due to their fast-dynamic response. Their monitoring is key to avoid downtime and economic losses. Even though a lot of research has been focused on incipient fault diagnosis in induction machines, early diagnosis in power electronic drive-fed machines is still a challenge. In this article, a novel two-level hybrid hierarchical convolution neural network with support vector machine (HCNN-SVM) is proposed for incipient interturn fault diagnosis of drive-fed machines. The first level of the proposed structure is intended to recognize the pattern of interturn fault in drive-fed IM, while the second level is developed to identify the fault severity. First, the effective features are extracted automatically using the shared layers of HCNN for fault diagnosis and fault severity evaluation at the same time. The features obtained using the HCNN are suitably used to train SVM for classification. Comparison of the results with the existing architectures, namely, HCNN and SVM, show the effectiveness of proposed hybrid method. The experimental results show that the hybrid HCNN-SVM is fast and highly accurate in identifying the interturn fault pattern and evaluating its severity.