国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理
关键词搜索：
| 标 题： |
A fault diagnosis method for rolling element bearings based on ICEEMDAN and Bayesian network
基于ICEEMDAN和贝叶斯网络的滚动轴承故障诊断方法 |
| 作 者： |
Liu, Zengkai
Lv, Kanglei
Zheng, Chao
Cai, Baoping
Lei, Gang
Liu, Yonghong |
| 关键词： |
Bayesian network [贝叶斯网络]
Energy entropy [ 能量熵]
Fault diagnosis [ 故障诊断]
ICEEMDAN [ ICEEMDAN]
REBs [ REBs]
EMPIRICAL MODE DECOMPOSITION [ 经验性分解模式]
RISK-ASSESSMENT [ 风险评估]
ALGORITHM [ 算法]
EXTRACTION [ 提取]
SYSTEM [ 系统]
NOISE [ 噪音] |
| 出版年： |
2022 |
| 期刊名： |
Journal of Mechanical Science and Technology
机械科学与技术杂志 |
| 年卷期： |
2022, vol.36, no.5 |
| 摘 要： |
As commonly used components in rotating machinery, rolling element bearings (REBs) can fail due to complex working conditions and high-speed rotation. The failure of bearings may cause great damage. It is necessary to identify the faults of bearings to prevent property losses and heavy casualties. This paper proposes a fault diagnosis approach based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Bayesian network. The intrinsic mode functions (IMFs) extracted by ICEEMDAN algorithm are applied to construct feature vectors based on the energy entropy, and then the fault diagnosis model of the bearing is constructed by Bayesian network. The influence of load and sampling frequency on diagnostic accuracy of the bearing with different fault types is studied in this paper. And the research results show that the ICEEMDAN-BN method can improve the uncertainty reasoning ability and accuracy of the developed fault diagnosis model.
在旋转机械中，滚动元件轴承（REBs）由于复杂的工况和高速运转而容易发生故障。轴承的失效可能造成重大财产损失和人员伤亡。因此，必须对轴承进行故障诊断，以避免经济损失和重大事故。本文提出一种基于改进型完全集合经验模态分解与自适应噪声（ICEEMDAN）结合贝叶斯网络的故障诊断方法。通过ICEEMDAN算法提取的固有模态函数（IMFs）用于构建基于能量熵的特征向量，进而利用贝叶斯网络构建轴承故障诊断模型。本文研究了载荷和采样频率对不同故障类型轴承诊断准确率的影响。研究结果表明，ICEEMDAN-BN方法能够提升所建故障诊断模型的不确定性推理能力和诊断精度。 |
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