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首页> 外文会议>International Symposium on Electrical Insulating Materials >Comparison of recognition rates between BP and ANFIS with FCM clustering method on off-line PD diagnosis of defect models of traction motor stator coil
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Comparison of recognition rates between BP and ANFIS with FCM clustering method on off-line PD diagnosis of defect models of traction motor stator coil

机译：基于FCM聚类方法对牵引电机定子线圈缺陷模型的FCM聚类方法对BP与ANFIS与FCM聚类方法的识别率的比较

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摘要

In this paper, we compared recognition rates between NN (neural networks) and clustering methods as a scheme of off-line PD (partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for recognition were acquired from PD detector. And then statistical distributions were calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP (back propagation algorithm) of NN and ANFIS (adaptive network based fuzzy inference system) using FCM (fuzzy clustering means) methods. So, classification rates of BP were somewhat higher than ANFIS performed preprocessing clustering method. But other items of ANFIS were better than BP; learning time, parameter number, capability on field, simplicity of algorithm.
机译：在本文中，我们将NN（神经网络）与聚类方法的比较识别率作为牵引电动机定子线圈发生的离线PD（局部放电）诊断方案。要获取PD数据，制作了三种缺陷型号。用于识别的PD数据是从PD检测器获取的。然后计算统计分布以分类模型放电来源。使用FCM（模糊聚类装置）方法作为NN和ANFIS（基于自适应网络的模糊推理系统）的两个分类工具，BP（反向传播算法）的输入数据。因此，BP的分类率略高于ANFI，执行预处理聚类方法。但其他ANFI的物品比BP好;学习时间，参数编号，现场功能，算法简单。

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