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首页> 外文期刊>Mechanical systems and signal processing >Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals
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Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals

机译：Dempster-Shafer理论在基于振动和电流信号的感应电动机故障诊断中的应用

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摘要

This paper presents an approach for the fault diagnosis in induction motors by using Dempster-Shafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications.
机译：本文提出了一种基于Dempster-Shafer理论的异步电动机故障诊断方法。从电动机定子电流和振动信号中提取特征，并减少数据传输。该技术使得在线应用成为可能。通过测量数据的选定特征对神经网络进行训练和测试。来自振动和电流分类器的分类结果的融合提高了诊断准确性。通过检测源自感应电动机的电动机电气和机械故障，证明了所提出系统的效率。测试结果证实了所提出的系统具有实时应用的潜力。

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