Incipient Inter-turn Fault Diagnosis in Induction motors using CNN and LSTM based Methods

Abstract

Induction machines are an integral part of any major industry or production process. Incipient fault diagnosis is an important topic which aims at detecting the fault at an early stage and isolating them from other ambiguous conditions. In this work, an analytical model for inter-turn fault diagnosis in induction machines has been developed. A methodology for early diagnosis of fault has been envisaged, in presence of ambiguous conditions such as voltage imbalances and load variations. The novel method is based on motor current signature analysis (MCSA), using deep learning based one dimensional convolutional neural network(1D-CNN) model and long short term model(LSTM). The results using these two methods have been compared, and this initial investigation shows that CNN is found to be more suitable than LSTM, for incipient fault diagnosis.