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Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines

1 Introduction</h5> Permanent Magnet Synchronous Machines (PMSM) are receiving increasing attention in robotic, automotive, electric traction and propulsion in Shipboard Power Systems (SPS) due to their high efficiency, high energy density and their suitability for high performance applications made possible by advancements in permanent magnet materials [1–3] . With the increased use of PMSM, efficient online condition monitoring and accurate machine fault diagnosis, for these machines, is very important. The persistence of the electromotive force, due to the permanent magnets, also puts a lot of stress on winding insulation and increases the possibility for degradation leading to winding faults. It has been established that SPS has conditions that promote the degradation of winding insulation [4] . This makes fault detection in these systems very important for the survivability, availability and efficient use of energy of rotating machines. In the published literature on fault diagnosis of electric machines, induction motors have been the primary focus [5–7] . However, research effort on PMSM fault diagnosis has been on the rise as more PMSMs enter the market for diverse application areas as enumerated above [8–13] .</P>Even though Artificial Intelligence (AI) was introduced a long time ago, it was in the early http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Electric Power Systems Research Elsevier
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