Research on sparsity indexes for fault diagnosis of rotating machinery
Research on sparsity indexes for fault diagnosis of rotating machinery
复制标题
旋转机械故障诊断稀疏指标研究
DOI：
10.1016/j.measurement.2020.107733
复制
发表时间：
2020-07
期刊：
影响因子：
5.6
通讯作者：
Jiadong Hua
中科院分区：
文献类型：
--
作者：
Yonghao Miao;Ming Zhao;Jiadong Hua
本文研究了旋转机械故障诊断的稀疏性指标。虽然各种稀疏性指标已被广泛应用于机械故障特征提取，有很少的信息的指导方针，可用于选择最佳的稀疏性指标，为指定的场景与不同的干扰。针对这一问题，本文首先分析了具有代表性的稀疏性指标，包括基尼系数、l2/l1范数、Hoyer测度和峰度。针对机械故障信号的特点，提出了数据长度无关性、抗随机脉冲性和故障脉冲可容性3个性能指标来定量评价稀疏性指标。在此基础上，总结了最优稀疏性测度的选取原则。在此基础上，将该准则用于峰度图和峰形图的改进，并对改进后的结果进行了评价。最后，比较结果，使用模拟和实验轴承故障信号，证实了一个最佳的方案，可以设计的稀疏性为基础的改进所提出的指导方针。
This paper originated from an investigation of sparsity indexes for fault diagnosis of rotating machinery. Although various sparsity indexes have been widely applied in machinery fault feature extraction, there is little information on the guideline available for the selection of the best sparsity index for the specified scenarios with different interferences. To solve the problem, this article firstly analyzes the performance of the representative sparsity indexes, containing Gini index,l2/l1norm, Hoyer measure and kurtosis. Aiming at the feature of the machinery fault signal, three performance attributes, including data-length independency, random-impulse resistance and fault-impulse discernibility, are originally proposed to quantitatively evaluate the sparsity index. Based on the comparison results, a guideline for the selection of the optimal sparsity measure is summarized. After that, this guideline is used for the improvement of kurtogram and protrugram, and the results are evaluated. Finally, the comparison result, using both simulated and experimental bearing fault signals, confirms that an optimal scheme can be designed for the sparsity-based improvement under the proposed guideline.
登录
查看更多内容
峰图流形学习及其在滚动轴承微弱信号检测中的应用
影响因子：
5.6
作者：
Wang Yi;Tse Peter W.;Tang Baoping;Qin Yi;Deng Lei;Huang Tao
通讯作者：
Huang Tao
影响因子：
8.4
作者：
H. Endo;R. Randall;C. Gosselin
通讯作者：
H. Endo;R. Randall;C. Gosselin
影响因子：
8.4
作者：
Endo, H.;Randall, R. B.
通讯作者：
Randall, R. B.
影响因子：
8.4
作者：
Antoni, J;Randall, RB
通讯作者：
Randall, RB
影响因子：
8.4
作者：
Barszcz, Tomasz;Jablonski, Adam
通讯作者：
Jablonski, Adam