Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis
Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis
复制标题
轴承故障诊断的非凸稀疏正则化和凸优化
DOI：
10.1109/tie.2018.2793271
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发表时间：
2018-01
影响因子：
7.7
通讯作者：
Xuefeng Chen
中科院分区：
文献类型：
--
作者：
Shibin Wang;Ivan Selesnick;Gaigai Cai;Yining Feng;Xin Sui;Xuefeng Chen
振动监测是轴承故障诊断最有效的方法之一，如何从含噪的振动信号中准确地估计出轴承故障信号是一个挑战。本文提出了一种基于广义极小极大凹（GMC）罚函数的非凸稀疏正则化轴承故障诊断方法，该方法保持了稀疏正则化最小二乘代价函数的凸性，从而可以通过凸优化算法求解全局最小值。此外，我们引入了一个k-稀疏策略的正则化参数的自适应选择。与传统滤波方法相比，广义矩量法的主要优点是能更好地保留轴承故障信号，同时减少噪声等分量的干扰，从而显著提高轴承故障信号的估计精度。仿真研究和两个运行故障实验验证了GMC在滚动轴承局部故障诊断中的有效性，对比研究表明GMC比L1范数正则化和谱峰度提供了更准确的估计结果。
Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.
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影响因子：
5.4
作者：
Selesnick, Ivan W.
通讯作者：
Selesnick, Ivan W.
影响因子：
4.5
作者：
Zhang, Cun-Hui
通讯作者：
Zhang, Cun-Hui
影响因子：
5.4
作者：
Selesnick, Ivan;Farshchian, Masoud
通讯作者：
Farshchian, Masoud
用于滚动轴承故障诊断的一类新的基于模型的脉冲小波及其稀疏表示
影响因子：
7.7
作者：
Qin, Yi
通讯作者：
Qin, Yi
影响因子：
7.7
作者：
Kang, Myeongsu;Kim, Jaeyoung;Kim, Jong-Myon
通讯作者：
Kim, Jong-Myon