组态式牵引电动机故障诊断模型

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (03): 402-405.

组态式牵引电动机故障诊断模型

聂冰a，赵慧敏a，丁鸣艳b，李文a

(大连交通大学 a. 软件学院； b. 电气信息学院，辽宁大连 116028)

收稿日期:2014-07-03 出版日期:2015-03-30 发布日期:2015-03-30
基金资助:
国家高技术研究发展计划（863）项目（2012AA040912），辽宁省教育厅高等学校科研计划项目（L2011077，

Configurable Fault Diagnosis Model in Induction Motor

NIE Binga,ZHAO Huimina,DING Mingyanb,LI Wena

(a. Software Technology Institute； b. School of Electronics and Information Engineer, Dalian Jiaotong University, Dalian 116028, Liaoning, China)

Received:2014-07-03 Online:2015-03-30 Published:2015-03-30

摘要/Abstract

摘要：

摘要：针对电动机典型的故障诊断模型网络结构复杂、训练困难等问题，提出一种组态式牵引电动机故障诊断模型.该模型由多个多输入单输出的子径向基函数神经网络构成，每个子模型识别一种故障特征.根据系统需要将多个子模型任意组合，用来识别类型繁多的电动机故障.利用特征提取后的样本数据对该模型进行训练，并通过测试样本验证了故障诊断模型的有效性.结果表明，采用组态式牵引电动机故障诊断模型，一个子模型仅识别一种牵引电动机故障状态，结构简单，模型训练难度小，提高了模型的故障识别能力以及应用的灵活性，为牵引电动机故障诊断提供了一条新思路.

关键词: , 牵引电动机, 故障诊断模型, 组态, 径向基函数神经网络, 构造

Abstract:

Abstract： Based on the research on typical fault diagnosis model, a configurable diagnosis model of induction motor was proposed to resolve the problem of complexity of network and difficulty of training. This model contains multiple sub RBF neural networks which have multiple inputs and single output, and one type of fault can be recognized by a specific sub-model. The sub-models can be any combination based on the demands of the system, and various faults can be identified. The model is trained using the samples with feature extracted, and the effectiveness of fault diagnosis model is verified through test samples. It is shown that one sub-model can be used to recognize one specific state of motor in the configured fault diagnosis model, the structure is simple, the difficulty of model training is reduced, the fault identification capability of model and flexibility of application are improved, providing a new method for the induction motor fault diagnosis.

Key words: induction motor, fault diagnosis model, configuration, radial basis function (RBF) neural network, structure

中图分类号:

TM 307

聂冰a，赵慧敏a，丁鸣艳b，李文a. 组态式牵引电动机故障诊断模型[J]. 上海交通大学学报（自然版）, 2015, 49(03): 402-405.

NIE Binga,ZHAO Huimina,DING Mingyanb,LI Wena. Configurable Fault Diagnosis Model in Induction Motor[J]. Journal of Shanghai Jiaotong University, 2015, 49(03): 402-405.

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