国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类
关键词搜索：
| 标 题： |
A New Method Based on Adaptive Treelets Transform for Fault Diagnosis of Rolling Bearing
基于自适应树列变换的滚动轴承故障诊断新方法 |
| 作 者： |
Hongfang Yuan
Xue Zhang
Xintao Xu
Huaqing Wang |
| 关键词： |
Treelets transform [树形结构正在变化]
Feature extraction [ 特征提取]
Rolling bearing [ 滚动轴承]
Fault diagnosis [ 故障诊断] |
| 出版年： |
2015 |
| 会议名： |
11th International Conference on Natural Computation (ICNC 2015)
第11届自然计算国际会议（ICNC 2015） |
| 届 次： |
11th |
| 摘 要： |
Features extracted from rolling bearing signal usually contain a great deal of noise and other redundant information which may reduce the efficiency of fault diagnosis. In this paper, a new method based on adaptive treelets transform is proposed for fault diagnosis of rolling bearing, which is proved to be an effective method. Treelets transform is a local multi-scale analysis method, and it is especially well-suited for high-dimensional and unordered signal with noise. Unlike the basic theory of treelets, this paper defines the similarity matrix from the point view of graph-theoretic based on geodesic distance, which can easily capture the intrinsic structure of original vibration signal of bearing. Because of the bearing signal collected by accelerometers cannot fully describe fault information, this paper select initial feature from the phase space. Treeles decomposition is then used for further feature extraction, which can not only reduce the disturbance of noise but also achieve the effect of dimension reduction. Finally, features are input to the model of SVM, the experimental results indicate that this proposed method can accurately identify different fault states of rolling bearing only by one-dimensional feature, and it is very robust to noise.
从滚动轴承信号中提取的特征通常包含大量噪声及其他冗余信息，这可能降低故障诊断的效率。本文提出一种基于自适应树列变换的新方法用于滚动轴承的故障诊断，并证明该方法具有良好的有效性。树列变换是一种局部多尺度分析方法，特别适用于高维且无序的带噪声信号。与传统的树列理论不同，本文从图论角度出发，基于测地距离定义了相似性矩阵，能够有效捕捉轴承原始振动信号的内在结构。由于通过加速度计采集的轴承信号无法完全描述故障信息，因此本文从相空间中选取初始特征，再利用树列分解进行进一步特征提取，不仅可减少噪声干扰，还能实现降维效果。最后，特征被输入到SVM模型中，实验结果表明，该方法仅通过单维特征即可准确识别滚动轴承的不同故障状态，并且对噪声具有很强的鲁棒性。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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