Condition monitoring of an induction motor stator windings via global optimization based on the hyperbolic cross points

The objective of condition monitoring of induction machines is to detect the incipient stage of a fault before serious damage occurs with high associated cost. Although the condition monitoring techniques have been intensively investigated in the last decades, research is still carried out in reducing cost and improving accuracy. This paper proposes a novel method that enables efficient and accurate monitoring of the stator winding circuit fault. The proposed method is based on the sparse grid optimization method applied in the least squares estimation of the circuit parameters that characterize the condition of a fault incipient. The kernel of the method is the efficient search for the objective function minimum on the grid created by using the hyperbolic cross points (HCPs). The system cost and complexity are minimized since the proposed method only requires voltage and current signals recorded at a machine terminal without any invasive or additional hardware circuitry. The proposed HCP algorithm is robust to supply voltage unbalance and motor loading state. The validity and effectiveness of the proposed scheme is experimentally tested on a three-phase 800-W 380-V induction motor.