Bearing Fault Detection for Doubly fed Induction Generator Based on Stator Current

Abstract

Bearing failure often occurs in a doubly fed induction generator. The fault diagnosis method based on the current signals has been attracted much attention. In this article, we propose different discrete digital models and their measure functions employing random theory. First, based on the raw current signals with the probability density function (PDF), a distributed discrete digital model is proposed; to avoid finding the PDF in the raw current signals, a discrete digital model of the moment feature and a discrete digital model of raw data are proposed. Then, six measurement functions are proposed as features of the discrete digital model for pattern recognition and condition monitoring of bearing. Finally, the effectiveness of the current method is demonstrated by comparing different signal processing methods.