Model-based fault detection of vacuum cleaner motors

A semi-physical model aimed for detection of incipient faults in electrical motors is presented. In order to gain high sensitivity to faults a physical model is combined with a black-box model based on Adaptive Network-based Fuzzy Inference System (ANFIS) as a corrective term. The method is applied to vacuum cleaner motors. The architecture and hybrid learning procedure is presented. In the first step, parameters of the physical model are identified by simple least-squares method. Then, the modelling error is compensated by adaptive network learning procedure. This way, the meaning of the physical parameters can be preserved. Diagnostic results show higher sensitivity to faults, which enables reliable fault detection. Consequently, false and missed alarm ratio is reduced as well.