effective approach to use artificial intelligence for detecting different faults in working electrical machines

Artificial intelligence (ai) shows good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. this paper proposes an effective approach to solve this problem by getting the required training data from exact simulation results. to evaluate this idea, the finite elements method is used to simulate a three-phase induction motor (im) in the healthy state as well as the stator inter-turn fault, broken rotor bar fault, and mixed eccentricity fault conditions. then, for every fault condition, some fault indices are extracted from the stator line current and used to arrange and train a suitable support vector machine (svm) model to detect and discriminate the fault condition. a similar im is prepared in the laboratory, where, its stator line currents are sampled and recorded under the healthy and the fault conditions, and the same fault indices are extracted from the stator currents. some penalties, which are determined by comparing experimental test results and corresponding simulation results in the healthy state, are applied to the experimentally attained values of the indices. the modified indices are then applied to the trained svm models, where, the attained results confirm the trained svm models are equally able to detect and discriminate the faults in the real ims.