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Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis

机译：递归未抽取小波包变换和DAG SVM用于感应电动机诊断

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摘要

This paper is focused on the design of a new approach dedicated to solve classification problems for the detection of broken rotor bar (BRB) fault in induction motors (IM). This new method finds its origins in a novel combination of both recursive undecimated wavelet packet transform (RUWPT) and directed acyclic graph support vector machines (DAG SVMs). Most often, BRB frequency components are hardly detected in the stator current due to its low magnitude and closeness to the supply frequency component. To overcome this drawback, the RUWPT is applied to extract one parameter able to detect the fault with arbitrary working conditions and a great concern of low load cases. Different multiclass support vector machines (MSVMs) methods are evaluated with respect to accuracy, number of support vectors, and testing time. The experimental results confirm that the DAG SVMs and Symlet wavelet kernel function are fast, robust, and give the best classification accuracy of 99%.
机译：本文专注于设计一种新方法的设计，该方法专用于解决分类问题，以检测感应电动机（IM）中的转子断条（BRB）故障。这种新方法的发现源于递归未抽取小波包变换（RUWPT）和有向无环图支持向量机（DAG SVM）的新颖组合。大多数情况下，由于BRB频率分量的幅度小且与电源频率分量接近，因此很难在定子电流中检测到BRB频率分量。为了克服这一缺点，RUWPT用于提取一个参数，该参数能够在任意工作条件下以及对低负载情况非常关注的情况下检测故障。针对准确性，支持向量的数量和测试时间，评估了不同的多类支持向量机（MSVM）方法。实验结果证明，DAG SVM和Symlet小波核函数具有快速，鲁棒性，并提供了99％的最佳分类精度。

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