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.

中文翻译：

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基于均值和PSO-BPNN的感应电动机多类故障诊断

本文提出了一种基于平均冲击值（MIV）的特征选择模型，以解决基于电流信号的感应电动机（IM）故障诊断。本文将粒子群优化算法（PSO）与反向传播神经网络（BPNN）相结合，对IM的当前信号进行分类。首先，本研究的目的是建立IM故障诊断系统。此外，这项研究提出了一种由MIV组成的特征选择过程，其目的是减少分类器输入特征的数量。其次，在分析IM的当前信号后，将特征提取为特征数据库，并通过PSO-BPNN模型建立故障诊断。最后，通过此功能选择过程将多余的功能删除，并建立分类器。结果表明，基于MIV的特征选择模型可以针对IM故障检测问题以30 dB和20 dB的信噪比有效过滤特征。另外，BPNN的计算时间也减少了，这对在线检测很有帮助。