基于JADE-EMD的滚动轴承故障检测
2021年电子技术应用第6期
冯平兴1，张洪波2
1.成都工业学院 网络与通信工程学院，四川 成都611731；2.成都信息工程大学 通信工程学院，四川 成都610225
摘要： 轴承故障分析在滚动传动系统中一直是研究的热点，传统的轴承故障诊断方法往往建立在苛刻的约束条件之上，如检测信号为单一的故障信号成分、既定的混合系统保持不变或者模型建立在无噪声的环境等。针对这些局限，结合了独立成分分析(Independent Component Analysis，ICA)方法，提出了一种基于特征矩阵联合相似对角化及经验模态分解(Joint Approximative Diagonalization of Eigen matrix-Empirical Mode Decomposition，JADE-EMD)的多故障动态盲分析技术。该方法的基本思想是基于多输入多输出的动态混合模型，利用四阶统计量对随机噪声的盲辨识特性，将滚动轴承正常工作时的平稳随机噪声看成一类常规的信号输入。
中图分类号： TN91
文献标识码： A
DOI：10.16157/j.issn.0258-7998.201019
中文引用格式： 冯平兴，张洪波. 基于JADE-EMD的滚动轴承故障检测[J].电子技术应用，2021，47(6)：71-76.
英文引用格式： Feng Pingxing，Zhang Hongbo. Fault test of rolling bearing based on JADE-EMD[J]. Application of Electronic Technique，2021，47(6)：71-76.
文献标识码： A
DOI：10.16157/j.issn.0258-7998.201019
中文引用格式： 冯平兴，张洪波. 基于JADE-EMD的滚动轴承故障检测[J].电子技术应用，2021，47(6)：71-76.
英文引用格式： Feng Pingxing，Zhang Hongbo. Fault test of rolling bearing based on JADE-EMD[J]. Application of Electronic Technique，2021，47(6)：71-76.
Fault test of rolling bearing based on JADE-EMD
Feng Pingxing1，Zhang Hongbo2
1.School of Network and Communication Engineering，Chengdu Technological University，Chengdu 611731，China； 2.School of Communication and Information Engineering，Chengdu University of Information Technology，Chengdu 610225，China
Abstract： Bearing fault analysis has been a research focus in rolling transmission system. However, the traditional bearing fault diagnosis technology is usually based on strict constraints, such as the detection signal is a single fault signal component, the established hybrid system remains unchanged, and the model is established in noise free situation. Aiming at the limitation of this problem, combined with the independent component analysis(ICA) method, this study proposes a multi fault dynamic blind analysis method based on joint approximate diagonalization of eigenmatrix empirical mode decision(JADE-EMD). The basic idea of this method is based on the dynamic transmission system with multi input and multi output. Because of the blind identification characteristics for random noise with fourth-order statistics, the stationary random noise of rolling bearing in normal operation works as a kind of conventional signal input. Then, the mixed signals received by the sensor are decomposed into independent components by dynamic blind source separation technology. Finally, the separated fault signals are decomposed by EMD, and the distribution results of several basic mode component functions(IMF) are obtained. Simulation results show that the method can effectively diagnose the rolling bearing with faults. Especially in the multi bearing drive system, it can effectively avoid the mutual interference between various fault signals. Compared with the traditional single direct detection method, it can further improve the accuracy of fault bearing analysis.
Key words : JADE-EMD；dynamic blind analysis；rolling bearing；fault diagnosis
0 引言
滚动轴承是转动传输系统中的关键机械零件之一，由于其表面光滑、滚道的尺寸精密，因而早期故障的振动信号往往相对微弱[1-4]，常常淹没在轴与齿轮的振动信号中，而轴承的工作状态直接关系到整个机械传输系统的正常运行。为了保障机械系统的正常且安全可靠的运行，避免因轴承故障而对系统引起的次生损害[5-9]，需要一种能动态监测并能有效的诊断滚动轴承的工作状况。本文的研究提出利用独立成分分析(Independent Component Analysis，ICA)和经验模态分解(Empirical Mode Decomposition，EMD)技术对轴承故障信号进行联合分析[10]，通过利用这两种信号处理技术的优点实现了对轴承故障信号的检测。
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作者信息：
冯平兴1，张洪波2
(1.成都工业学院 网络与通信工程学院，四川 成都611731；2.成都信息工程大学 通信工程学院，四川 成都610225)
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