Analysis of Inter-Turn Short-Circuit Faults in Brushless DC Motors Based on Magnetic Leakage Flux and Back Propagation Neural Network

Abstract

Inter-turn short-circuit (ITSC) faults are the most common fault types of brushless DC (BLDC) motors used in industry. Fault diagnosis of BLDC motors is of critical importance due to their wide spread applications. Existing diagnosis methods are based on voltage and current analysis which is useful but difficult to identify early faults and fault locations, as these suffer from external disturbances. This paper proposes a new offline method for fault detection based on magnetic leakage flux (MLF) and backpropagation neural network (BPNN) for improving the level of fault diagnosis. The ITSC and MLF are modeled and analyzed theoretically. Then they are verified by finite element analysis (FEM) and experimental tests. Hall sensors are used to form an array to collect MLF signals at different positions outside the test motor. The frequency-domain characteristic matrix of MLF signals is analyzed by BPNN models. The experimental results show that the proposed method can effectively detect ITSCs, and estimate the fault degree and the location of the fault. The method is a promising technology as it is non-intrusive and accurate.