Bearing fault detection in induction motors using MCSA and statistical analysis

Abstract

Due to its straightforward installation, operation and low cost, the Induction Motor (IM) is widely used in industry. Because of its constant service, there is a growing necessity of fault detection techniques for IM. Bearings fault (BF) is a common fault that may appear in an induction motor, and it may cause severe damage to the device. Thus, early detection is necessary to program the corresponding maintenance and extend the service of the machine and to reduce expenses for maintenance service or replacement. In literature, different techniques have been proposed to detect this fault involving a signal preprocessing step and applying time or frequency domain approaches, which require time and resource consumption. In this paper, a technique to detect BF based on Motor Current Signal Analysis (MCSA) via a statistical analysis named Kolmogorov-Smirnov Test (K-S test), is presented. K-S test determines if two samples come from the same distribution by measuring the maximum distance between them. We compared signals of two motor conditions, no damage (ND) and BF. We applied the test to the raw current signal.