An improvement of a diagnosis procedure for AC machines using two external flux sensors based on a fusion process with belief functions

In this paper, a method for diagnosis of AC machines using the spectrum of the near magnetic field is presented. The method is associated to a fusion process based on belief functions which analyze the measurements. In previous works, it has been shown that it is possible to detect the inter-turns short circuit in the stator windings of electrical machines using a noninvasive method. It is based on the analysis of the variation of sensitive harmonics when the load varies, and eliminates the main drawback presented by other diagnostic methods which use the comparison with a healthy state assumed known. Several measurements around the machine are necessary to increase the probability of the fault detection because the fault position relatively to the sensor can strongly influence the results. So in this paper it is proposed to exploit conjointly the whole measurements in order to obtain a more robust and reliable diagnostic and to increase the probability of detecting the fault. The merging of the different estimations being realized through the belief functions framework, this approach is tested on real measurements. Experimental tests are performed on a special rewound induction machine in order to validate the theoretical approach.