Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis
Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis
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DOI：
10.1109/tii.2015.2462315
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发表时间：
2015-07
影响因子：
12.3
通讯作者：
H. Keskes;A. Braham
中科院分区：
文献类型：
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作者：
H. Keskes;A. Braham
针对感应电机转子断条故障的分类问题，设计了一种新的故障检测方法。这种新方法起源于递归非抽取小波包变换(RUWPT)和有向无环图支持向量机(DAG SVM)的一种新组合。通常，定子电流中的BRB频率分量很难检测到，因为它的幅值很小，而且与电源频率分量很接近。为了克服这一缺陷，采用RUWPT来提取一个参数，该参数能够在任意工作条件下检测出低负载情况下的故障。对不同的多类支持向量机(MSVM)方法在精度、支持向量个数和测试时间方面进行了评估。实验结果表明，DAG支持向量机和Symlet小波核函数具有快速、稳健的特点，最高分类正确率可达99%。
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%.