Data-Driven Fault Detection of Electrical Machine

For the purpose of monitoring the health conditions of electrical machines, a framework is proposed to establish the methods to provide an early warning to potential machine failures in data mining terminology. The framework consists of five stages including data segmentation, feature extraction/selection, multi-classifier ensemble, decision fusion and output, which is flexible and can be adapted for any known faults. The difference lies in the implementation choices of techniques and structures (e.g. number of classifiers) in the second to forth stage as well as the input requirements. As an example, the turn-to-turn short circuit fault of induction motor is used as the known fault in studies in this work. Simulation results show the effectiveness of the proposed techniques.