A New Machine Learning Method based on PCA and SVM
A New Machine Learning Method based on PCA and SVM
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DOI：
10.1109/iccias.2006.294119
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发表时间：
2006-11
期刊：
影响因子：
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通讯作者：
R. Zhao;Hao Zhang;Jiangfeng Lu;Cuiling Li;Hui Zhang
中科院分区：
文献类型：
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作者：
R. Zhao;Hao Zhang;Jiangfeng Lu;Cuiling Li;Hui Zhang
在故障模式识别领域，实时在线故障诊断是高速机床提出的新要求，也是一个宏伟的研究方向。分类的精度和速度是这类智能故障诊断中故障模式识别的重要研究问题。虽然已经提出了许多改进的人工神经网络方法，但大多数方法只关注分类精度，而不是计算速度。本文介绍了一种用于故障诊断的支持向量机模型，并分析了该模型对噪声数据敏感性的局限性。为了解决这一问题，我们引入了主成分分析(PCA)方法来降低样本集的维度，并对机器采样的数据进行去噪。此外，对于整个实时数据处理和故障模式识别，采用了一种小波包分析的方法，将现场传感器信号在时间频谱上实时转换为频谱对应频率段的能量值。为此，我们在故障诊断领域提出了一种新的机器学习方法：基于小波包分析的主成分分析支持向量机方法。与其他典型网络相比，这种改进的方法在分类精度和计算速度上都有很大的提高。并对整个处理流程进行了详细说明。最后，对数控磨床的故障诊断结果表明，该方法具有分类精度高、运算速度快的特点。证明了小波包分析、主成分分析和支持向量机的有机结合是一种新的、有效的、实用的方法，特别是对于实时在线准确的故障诊断
In fault pattern recognition field, the real-time online fault diagnosis is a new requirement especially from the high-speed machines, and also the magnificent researching direction. The precision and speed of the classification are important research issues in fault pattern recognition for this kind of intelligent fault diagnosis. Although many improved ANN (artificial neural net) methods have been proposed for this purpose, most approaches focus only on the classification precision, instead of the computing speed. In this paper, a SVM model for fault diagnosis is introduced and analyzed about its limitation from sensibility to noisy data. To address this problem, we introduce the PCA (principal component analysis) method to reduce the dimension of the sample set and de-noise data sampled from the machine. Furthermore, for the whole real-time data processing and fault pattern recognition, a kind of wavelet packet analysis is applied to translate the field sensor signal in time-spectrum into the energy value in frequency-spectrum corresponding frequency segments real-timely. Therefore we present a new machine learning method: PSVM (primary component analysis support vector machine) method based on wavelet packet analysis in the fault diagnosis field. And this improved method particularly betters the precision and computing speed of the classification then other typical networks. And also the whole processing workflow is illustrated in details. Finally the diagnosis result of the CNC grinding machine demonstrates this method with both high classification precision and quick computational speed. And the whole integration of wavelet packet analysis, PCA, and SVM is proved a new, effective and practical approach especially for the real-time online fault diagnosis exactly