Adaptive diagnosis by pattern recognition: Application on an induction machine

In this paper, a pattern recognition method is used to provide the tracking and the diagnosis of a system. To illustrate it, we used as application, an asynchronous motor 5.5 kW with squirrel-cage, in particular for the detection of broken bars, under any level of load. From measurements carried out on the system, parameters are calculated. These parameters are used to build up a pattern vector which is considered as the system signature. To determine this pattern vector, two methods are applied. One, well-known, sequential backward selection (SBS) and the other, which we developed, based on a genetic approach, with the advantage to determine the optimal dimension of the representation space and to give better results (value of criterion) than SBS. The determination of the decision space is carried out using a method of automatic classification called clustering. The decision phase is based on the ldquok-nearest neighborsrdquo rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states. The appearance of a new operating mode is taken into account in order to enrich the initial knowledge base and thus to improve the diagnosis.