Incipient turn fault detection and condition monitoring of induction machine using analytical wavelet transform

Abstract

Diagnosis and monitoring the condition of induction machines is critical for industries. Incipient fault detection has received a lot of attention in recent years. In this paper, a method based on complex wavelets is proposed for incipient fault detection and condition monitoring. A Complex wavelet-Support vector machine (SVM) classifier is developed which takes into account four conditions i.e.: healthy, turn fault (TF) under balanced supply conditions, voltage imbalance and interturn fault with voltage imbalance, both occurring at same time. The performance metrics show the ability of the technique to identify the fault at an early stage and it also provides additional information regarding which of the four conditions is prevailing at a given time. Voltage imbalance and turn fault are often confused. Both affect the performance of the machine and the unbalanced voltage condition considerably reduces the winding insulation life due to overheating. A comparison with standard Discrete Wavelet Transform (DWT) shows the effectiveness of the method in providing reliable information under variable supply-frequency conditions.