Research on feature extraction algorithm of rolling bearing fatigue evolution stage based on acoustic emission
Research on feature extraction algorithm of rolling bearing fatigue evolution stage based on acoustic emission
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基于声发射的滚动轴承疲劳演化阶段特征提取算法研究
DOI：
10.1016/j.ymssp.2017.08.001
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发表时间：
2017-09
影响因子：
8.4
通讯作者：
Cao Yongli
中科院分区：
文献类型：
--
作者：
Wang Zhihai;Wu Xing;Liu Xiaoqin;Xie Jinkui;Cao Yongli
本文主要研究从监测信号中提取滚动轴承有效的演化阶段特征。每个特征对不同的疲劳演化阶段具有不同的损伤敏感性。疲劳演化信息分散在不同的特征中，增加了疲劳阶段识别的难度。针对这一问题，提出了一种新的滚动轴承声发射信号特征提取方法，并利用专门设计的试验台进行了实验验证。该方法将小波包去噪（WPD）和改进的核熵分量分析（KECA）相结合。首先，对原始信号进行WPD去噪;其次，将高斯核函数的KECA方法应用于去噪后的信号中提取的特征矩阵。提出了一种基于惯性权重和动态加速常数的最佳核熵分量数理论的粒子群优化算法（BWCPSO）。BWCPSO方法以最小化核熵分量数和最大化滚动轴承的级信息为目标。最优核参数使KECA方法能够更好地提取和收敛原始信号信息。最后，每个疲劳演化阶段可以自适应识别的主核熵得分（KES）图。实验结果表明，该方法能有效地提取滚动轴承的疲劳演化阶段信息，且比传统的特征趋势分析和其他两种传统的特征提取方法简单、准确。
This paper focuses on extracting effective evolution stage features of rolling bearing from the monitoring signal. Each feature has a different damage sensibility to different fatigue evolution stages. Fatigue evolution information is dispersed in different features, which increases the difficulty to recognize the fatigue stages. This paper presents a new feature extraction method for acoustic emission (AE) signal of rolling bearing to solve the problem, and a specially designed test rig is used for the experimental verification. The new method combines wavelet packet de-noising (WPD) with an improved kernel entropy component analysis (KECA). First, de-noising original signal by WPD method. Second, applying KECA method with Gaussian kernel function on the feature matrix extracted from the de-noised signal. A new particle swarm optimization method based on the best kernel entropy component number theory with inertia weight and dynamic accelerating constant (BWCPSO) is proposed to optimize the kernel parameter. BWCPSO method puts the minimized kernel entropy components number with the maximum stage information of rolling bearing as its objective. The optimal kernel parameter can make KECA method extract and converge the original signal information greatly. Finally, each fatigue evolution stage can be identified adaptively by the main kernel entropy score (KES) graphs. The experiment results show that the proposed method extracts the fatigue evolution stages information of rolling bearing effectively and much easier and more accuracy than the traditional feature trend analysis and other two traditional feature extraction methods.
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影响因子：
3.1
作者：
Jin Xiao;Miguel A. López;Xiaoguang Hu;Jizhong Xiao;Fei Yan
通讯作者：
Jin Xiao;Miguel A. López;Xiaoguang Hu;Jizhong Xiao;Fei Yan
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
--
发表时间：
2011
期刊：
Journal of Vibration and Shock
影响因子：
--
作者：
Lin Zi-nan
通讯作者：
Lin Zi-nan
DOI：
10.1016/s0167-8922(08)71045-9
发表时间：
1987
期刊：
Tribology and Interface Engineering Series
影响因子：
--
作者：
T. Yoshioka;T. Fujiwara
通讯作者：
T. Yoshioka;T. Fujiwara