Induction motor bearings diagnostic indicators based on MCSA and normalized triple covariance

Abstract

Induction motors are one of the most widely used electrical machines. Statistics of bearing failures of induction motors indicate, that they constitute more than 40% of induction motor damage. Therefore, bearing diagnosis is so important for trouble-free work of induction motors. The most common methods of bearing diagnosis are based on vibration signal analysis. The main disadvantage of those methods is the need for physical access to the diagnosed machine, which is not always possible. Methods based on motor current signature analysis are free of this disadvantage. Preliminary studies have shown that motor current signature analysis based normalized triple covariance is a very good diagnostic indicator for induction motor bearings. This paper presents an attempt to find a more accurate diagnostic indicator based on normalized triple covariance. In this paper the author verifies how many diagnostic features (normalized triple covariances) included in diagnostic indicator can give better separation between healthy and unhealthy cases.