An automated thermographic image segmentation method for induction motor fault diagnosis

Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

I. Introduction

Induction motors are probably the most reliable rotating electrical machines [1]. Nonetheless, their participation in a vast number of industrial processes and applications, some of them critical, justifies the importance of developing suitable maintenance programs adapted to these machines. Within the induction motor predictive maintenance area, several techniques have been developed over recent decades that are intended to diagnose a wide range of eventual failures in these machines, when faults are still in their early stages of development. Emphasis has been put in the non-invasive nature of the developed diagnosis methods, since in many applications no interference with the machine operation is desired.