Early Stator Fault Detection and Condition Identification in Induction Motor Using Novel Deep Network

Fault diagnosis of induction motor is an important task for both the researchers and industry. The interturn fault is one of the frequented faults and accounts for more than 37% of the failures. Thus, the diagnosis of interturn fault at an early stage has become vital to avoid the catastrophic failures and production losses. In this work, the advantages of deep learning-based methods are explored to detect the interturn fault at an incipient stage. Hybrid architectures has been proposed in this work for incipient inter turn fault diagnosis [i.e., 1D convolution neural network-long short-term memory (1DCNN-LSTM) and 1DCNN-gated recurrent unit (GRU) based methods]. The performance of the hybrid methods has been compared with standalone techniques (1DCNN, LSTM, and GRU), and the results show that the hybrid models (1DCNN-LSTM and 1DCNN-GRU) outperform the individual architectures (in terms of accuracy, sensitivity, and specificity), for fault diagnosis and isolation. The computational time has also been found to be comparable, which is an added advantage for fast diagnosis tasks. The approach combines the feature extraction and classification tasks, to perform early diagnosis, in presence of ambiguous conditions. Here, the incipient diagnosis of fault has been augmented with condition identification i.e., healthy (balanced voltages), healthy (unbalanced voltages), faulty (balanced voltages), and faulty (unbalanced voltages), to effect both early detection and isolation. The performance metrics show the robustness of the proposed method for not only detecting fault, but also identifying such confusing conditions for critical applications, in which induction motors are employed.