Numerical magnetic field analysis and signal processing for fault diagnostics of electrical machines

Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip-ring generator having different faults implemented in their structure. Virtual measurement data provided by the numerical magnetic field analysis are analysed using modern signal processing techniques to get a reliable indication of the fault. Support vector machine based classification is applied to fault diagnostics. The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection. Introduction Companies dealing with electrical machinery find condition monitoring and diagnostics more and more important. The supervision of electrical drive systems using non-invasive condition monitoring techniques is becoming a state-of-the-art method for improving the reliability of electrical drives in many branches of the industry. Typical questions are, how to detect a starting fault, how to distinguish a deteriorating fault from a harmless constructional asymmetry, which are the physical quantities that best indicate a fault and how to measure them, and how should the measured signals be processed to get the most reliable diagnosis. The basis of any reliable diagnostic method is an understanding of the electric, magnetic and mechanical behavior of the machine in healthy-state and under fault conditions. The aim of computer simulation of magnetic field The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/researchregister http://www.emeraldinsight.com/0332-1649.htm Numerical magnetic field analysis