Stator Turn Fault Diagnosis and Severity Assessment in Converter-Fed Induction Motor Using Flat Diagnosis Structure Based on Deep Learning Approach

This work aims to explore the hybrid deep learning techniques for early interturn fault diagnosis and fault severity assessment in variable frequency induction motor drive. However, the existing literature mostly focuses on identifying the fault patterns, whereas the assessment of fault deterioration before it reaches the extreme degradation state has been less investigated. In this article, a hybrid structure combining 2-D convolution neural network (2DCNN) and long-short term memory/gated recurrent unit (LSTM/GRU) has been modeled as a relatively lightweight flat diagnosis structure (FDS) for early fault identification and its severity assessment simultaneously in converter-fed induction motor. Initially, the hierarchical layers of 2DCNN extract the effective features automatically for fault detection and its severity identification simultaneously. Then, these features are presented as suitable inputs to LSTM/GRU for classification. A comparative evaluation of the proposed hybrid method with the standalone structure, namely, 2DCNN, LSTM, and support vector machine (SVM), proves the advantages of the proposed scheme and its applicability for both fault diagnosis and fault severity assessment in industrial drive systems, under variable frequencies and load variations.