国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类
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| 标 题： |
A novel intelligent approach based on WOAGWO-VMD and MPA-LSSVM for diagnosis of bearing faults
基于WOAGWO-VMD和MPA-LSSVM的轴承故障诊断新智能方法 |
| 作 者： |
Taibi, Ahmed
Ikhlef, Nabil
Touati, Said |
| 关键词： |
Fault diagnosis [故障诊断]
Feature extraction [ 特征提取]
WOAGWO [ WOAGWO]
Variational mode decomposition [ 变分模分解]
MPA [ MPA]
LSSVM [ LSSVM]
DECOMPOSITION [ 分解[%$#@*]吉耶尔大学吉耶尔大学核研究中心比里内] |
| 出版年： |
2022 |
| 期刊名： |
The International Journal of Advanced Manufacturing Technology
先进制造技术国际期刊 |
| 年卷期： |
2022, vol.120, no.5/6 |
| 摘 要： |
The bearing fault diagnosis plays an important role to reduce catastrophic failures and ensure the continuity of running machines to avoid heavy economic loss. The vibration signals of rolling bearings are often nonlinear and nonstationary; it is difficult to extract sensitive features and diagnose faults by traditional signal processing methods. To solve this problem, a novel intelligent fault-diagnosis approach based on whale optimization algorithm grey wolf optimization-variational mode decomposition (WOAGWO-VMD) algorithm and the marine predators algorithm optimization-least squares support vector machine (MPA-LSSVM) is proposed in this paper. Firstly, hybrid algorithm WOAGWO is used to optimize the parameters of VMD and obtain the optimal combination (K; alpha). Then, the optimized VMD algorithm is utilized to decompose the vibration signal of the rolling bearing into several intrinsic mode functions, and a new sensitive indicator is created to select the components containing the most information. For these components, the dispersion entropy feature, permutation entropy feature, and singular value feature are extracted to form the multi-feature vector. Finally, the feature vectors obtained are input to the MPA-LSSVM for diagnosis and identification. The validity and strength of the proposed method is verified by experimental data under different bearing conditions. The results have shown that the proposed method can extract the fault feature information of 16 bearing signals of different fault types effectively and identify them accurately.
轴承故障诊断在减少重大故障、确保设备连续运行、避免巨大经济损失方面发挥着重要作用。滚动轴承的振动信号通常具有非线性和非平稳性； 传统信号处理方法难以提取敏感特征并诊断故障。为解决这一问题，本文提出了一种基于鲸鱼优化算法、灰狼优化-变分模式分解（WOAGWO-VMD）算法以及海洋捕食者算法优化-最小二乘支持向量机（MPA-LSSVM）的新型智能故障诊断方法。首先，采用混合算法WOAGWO优化VMD的参数，获得最优组合（K； 然后，采用优化的VMD算法将滚动轴承的振动信号分解为多个固有模态函数，并构建一种新的灵敏指标以选择包含最多信息的分量。针对这些分量，提取分散熵特征、排列熵特征和奇异值特征，形成多特征向量。最后，将所得特征向量输入MPA-LSSVM模型进行诊断与识别。通过不同轴承工况下的实验数据验证了该方法的有效性和优越性。结果表明，所提出的方法能够有效提取16种不同类型轴承信号的故障特征信息，并实现准确的故障识别。 |
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