A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Abstract

Abstract Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

Keywords

• Induction motors (IMs)
• Stator inter-turn fault (SITF)
• Voltage quality problem
• Principal component analysis (PCA)