Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN

This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection.

Automated summary

This paper presents a feature selection model based on mean impact value (MIV) for induction motor fault diagnosis using the current signal. The model effectively filters features, establishes a classifier, and reduces the computing time of BPNN.