| 基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法 |
| |
| 引用本文： | 张敏,蔡振宇,包珊珊.基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法[J].西南交通大学学报,2019,54(3):633-639, 662. |
| |
| 作者姓名： | 张敏 蔡振宇 包珊珊 |
| |
| 作者单位： | 西南交通大学机械工程学院;西南交通大学轨道交通运维技术与装备四川省重点实验室 |
| |
| 基金项目： | 中央高校基本科研业务费专项资金资助项目（2682016CX031）、国家自然科学基金项目（51675450） |
| |
| 摘 要： | 为有效提取非平稳特性的滚动轴承振动信号特征，提高故障诊断效率，提出一种采用集合经验模态分解（empiricalmode?decomposition，EEMD）、Hilbert变换的特征提取方法，并利用烟花算法优化支持向量机（support vector machine，SVM）分类参数的滚动轴承故障诊断方法. 通过EEMD方法将目标信号分解成若干个模态函数，采取Hilbert变换获取模态函数的瞬时频率，并对模态函数及其瞬时频率进行统计特征提取，从而实现特征的有效降维. 结果表明:信号经过EEMD-Hilbert处理后特征能有效提取，将训练集和测试集各600组数据代入烟花算法优化SVM模型得到测试集正确率为99.63%；比传统的遗传算法和粒子群算法优化模型分别提高0.4%和0.2%左右；同时收敛时间更短，验证了该算法模型的可行性与有效性.
|
| 关 键 词： | 集合经验模态分解 Hilbert变换 烟花算法 支持向量机 |
| 收稿时间： | 2017-06-14 |
Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM |
| |
| ZHANG Min,CAI Zhenyu,BAO Shanshan.Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM[J].Journal of Southwest Jiaotong University,2019,54(3):633-639, 662. |
| |
| Authors: | ZHANG Min CAI Zhenyu BAO Shanshan |
| |
| Abstract: | To effectively extract the non-stationary characteristics of the rolling bearing vibration signal and improve the fault diagnosis efficiency, a feature extraction method based on the ensemble empirical mode decomposition (EEMD) and Hilbert transform was proposed. The support vector machine (SVM) classification parameters were optimised using the fireworks algorithm (FWA) for the rolling bearing fault diagnosis method. The EEMD method was used to decompose the target signal into several modal functions. The instantaneous frequencies of the modal functions were obtained through Hilbert transforms. Statistical feature extraction and dimensionality reduction were respectively performed for the modal function and instantaneous frequency. The fireworks algorithm model was used to optimise the SVM parameters as well as the multi-classification fault diagnosis with training and test sets drawn from 600 datasets. The accuracy of the signal is estimated to be 99.633%, which is 0.4% and 0.2% higher than that of the traditional genetic algorithm and particle swarm optimisation algorithm, respectively. Further, the ability of iterative convergence is also seen to have obvious advantages. The feasibility and validity of the algorithm models are thus verified. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《西南交通大学学报》浏览原始摘要信息 |
|
点击此处可从《西南交通大学学报》下载全文 |
