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首页> 外文期刊>IEEE transactions on industrial informatics >A New Demagnetization Fault Recognition and Classification Method for DPMSLM
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A New Demagnetization Fault Recognition and Classification Method for DPMSLM

机译：DPMSLM的新的退磁故障识别与分类方法

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摘要

This paper investigates a new method for the demagnetization fault recognition and classification in double-sided permanent magnet synchronous linear motors that are used in linear motion applications. This method is based on time-time-transform (TT) coupled with extreme learning machine (ELM), which are especially suitable for the industrial occasions such as motor batch demagnetization inspection before delivery and periodic maintenance. First, a finite element analysis model with demagnetization faults is built to extract three lines (up-line, center-line, and down-line) magnetic flux density signals. Second, TT is first applied to conduct magnetic signals waveform transformation, and digital picture processing technology is innovatively used to extract the pixel rate of its diagonal elements contour surfaces as the fault feature. Then, machine learning algorithm called ELM is utilized as a classifier to obtain the unique fault labels that can represent the demagnetization occurred positions, sides, and severity types in detail. The validity and superiority of ELM is verified through comparison with back propagation neural network, and probabilistic neural network. Finally, prototype motor experimental platform is designed to confirm the correctness and effectiveness of this proposed method.
机译：本文研究了在线性运动应用中使用的双面永磁同步线性电动机的退磁故障识别和分类的新方法。该方法基于与极端学习机（ELM）耦合的时代变换（TT），其特别适用于在交付之前的电机批量退磁检查等工业场合。首先，建立具有退磁故障的有限元分析模型以提取三条线（上线，中心线和下线）磁通密度信号。其次，首先应用TT来传导磁信号波形变换，并且数字图像处理技术是创新的，用于提取其对角线元件轮廓表面的像素率作为故障特征。然后，称为ELM的机器学习算法用作分类器以获得可以详细地表示失磁发生的唯一故障标签。通过与后传播神经网络和概率神经网络的比较来验证ELM的有效性和优越性。最后，原型电动机实验平台旨在确认这一提出方法的正确性和有效性。

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