Information fusion of external flux sensors for detection of inter-turn short circuit faults in induction machines

Abstract

This paper presents a method based on fusion technique applied to signatures obtained from external stray flux to detect an inter turn short circuits in induction machines. This technique uses the belief functions framework to represent and merge the information about short circuits obtained from sensors placed around the machine to be diagnosed. The influence of the sensors positions around the machine to detect faults is studied. This fusion technique leads to a new diagnosis method, which only uses the information captured from the stray magnetic field around the machine, having then the advantage of being non-invasive. Six external flux sensors placed on a belt fixed around the machine provide information used for the diagnostic technique. These signatures are obtained by experimental tests using a rewound induction machine that allows one to create inter-turn short circuit faults with different severity levels.