Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension
Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension
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基于SVM和分形维数的滚动轴承智能故障诊断
DOI：
10.1016/j.ymssp.2006.10.005
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发表时间：
2007-07-01
影响因子：
8.4
通讯作者：
Zhu, Yongsheng
中科院分区：
文献类型：
--
作者：
Yang, Junyan;Zhang, Youyun;Zhu, Yongsheng
非线性动力学理论的发展为识别和预测复杂的非线性动力学行为带来了新的方法。分形维数可以定量地描述振动信号的非线性行为。本文利用容量维、信息维和相关维对滚动轴承的各种故障类型进行分类和评估各种故障状态，并利用支持向量机评估各个分形维及其组合的分类性能。对10个故障数据集的实验表明，单一分形维数在大多数数据集上的分类性能都很差，并且对于给定的数据集，每个分形维数表现出不同的分类能力，这表明不同的分形维数包含了不同的故障信息。对不同分形维数组合的实验表明，这三种分形维数的组合得分最高，但在某些数据集上分类性能仍然较差。为了进一步提高SVM的分类性能，引入时域统计特征与三个分形维度一起训练SVM，SVM的分类性能得到显着提高。同时，实验结果表明，使用I I时域统计特征和三个分形维度串联训练的SVM的分类性能优于仅使用I I时域统计特征或三个分形维度训练的SVM。 (c) 2006 Elsevier Ltd. 保留所有权利。
The development of non-linear dynamic theory brought a new method for recognising and predicting the complex nonlinear dynamic behaviour. Fractal dimension can quantitatively describe the non-linear behaviour of vibration signal. In the present paper, the capacity dimension, information dimension and correlation dimension are applied to classify various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of each fractal dimension and their combinations are evaluated by using SVMs. Experiments on 10 fault data sets showed that the classification performance of the single fractal dimension is quite poor on most data sets, and for a given data set, each fractal dimension exhibited different classification ability, this indicates that various fractal dimensions contain various fault information. Experiments on different combinations of the fractal dimensions demonstrated that the combination of all these three fractal dimensions gets the highest score, but the classification performance is still poor on some data sets. In order to improve the classification performance of the SVM further, I I time-domain statistical features are introduced to train the SVM together with three fractal dimensions, and the classification performance of the SVM is improved significantly. At the same time, experimental results showed that the classification performance of the SVM trained with I I time-domain statistical features in tandem with three fractal dimensions outperforms that of the SVM trained only with I I time-domain statistical features or with three fractal dimensions. (c) 2006 Elsevier Ltd. All rights reserved.