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KNN based fault diagnosis system for induction motor

机译：基于KNN的感应电动机故障诊断系统

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摘要

The paper deals with an online fault diagnosis system of 3-¿¿ induction motor with sequence component analysis. The fault diagnosis system uses only time synchronized three phase stator voltage and current samples, from which the positive and negative sequence components have been calculated using Sample Shifting Technique (SST). With the objectives to detect the type of fault, fault severity and faulty phase using sequence components analysis. The computational technique like K-nearest neighbor algorithm has been utilized to enhance the accuracy in diagnosis of faulty phase and the severity of fault. The severity information will definitely help to make a machine maintenance schedule and accordingly plant shut down can be made minimum.
机译：本文研究了一种基于序列成分分析的3-¿感应电动机在线故障诊断系统。故障诊断系统仅使用时间同步的三相定子电压和电流样本，已使用样本移位技术（SST）从中计算出正序分量和负序分量。目的是使用顺序分量分析来检测故障类型，故障严重性和故障阶段。诸如K近邻算法之类的计算技术已被用于提高故障相诊断的准确性和故障严重性。严重性信息无疑将有助于制定机器维护计划，并因此可以最大程度地减少工厂停机的时间。

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