Induction machine fault detection using stray flux EMF measurement and neural network-based decision

The aim of this paper is to present the performances of voltage unbalance and rotor fault detections using an external stray flux sensor in a working three-phase induction machine. The automatic classification and fault severity degree evaluation are realized by using a neural network approach based on a multi-layer perceptron (MLP) structure. In this paper, it is proved that a simple external stray flux sensor is more efficient than the classical stator current sensor to detect rotor broken bar and voltage unbalance, using data processing at low-frequency resolution.