| 周建民,李家辉,尹文豪,游 涛,熊文豪,高 森.基于 CEEMDAN 和 PSO-OCSVM 的
滚动轴承性能退化评估[J].电子测量与仪器学报,2021,35(7):194-201 |
| 基于 CEEMDAN 和 PSO-OCSVM 的
滚动轴承性能退化评估 |
| Evaluation of rolling bearing degradation performancebased on CEEMDAN and PSO-OCSVM |
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| DOI： |
| 中文关键词: 滚动轴承 CEEMDAN 粒子群优化算法 单分类支持向量机 性能退化评估 |
| 英文关键词:rolling bearing CEEMDAN particle swarm optimization algorithm one class support vector machine performance
degradation assessment |
| 基金项目:国家自然科学基金（51865010）项目资助 |
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| 摘要点击次数: 1012 |
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| 中文摘要: |
| 为了提高单分类支持向量机(one class support vector machine, OCSVM)在滚动轴承性能退化评估的准确性,提出了一种
基于具 有 自 适 应 白 噪 声 的 完 备 经 验 模 态 分 解 方 法 ( complete ensemble empirical mode decomposition with adaptive noise,
CEEMDAN)、粒子群优化算法(particle swarm optimization, PSO)和 OCSVM 相结合的性能退化评估方法。 首先采用 CEEMDAN
将采集的振动信号计算展开为多个固有模态函数(intrinsic mode functions,IMFs),根据 IMFs 的能量密度获得典型的特征信号;
其次,通过粒子群算法优化 OCSVM 的参数 ν 和径向基核函数参数 g,增强 OCSVM 的学习能力和泛化能力;最后,使用 3σ 设置
自适应阈值,确定轴承早期失效阈值并用 CEEMDAN 和 Hilbert 包络解调的方法验证评估结果的正确性,采用辛辛那提大学的
轴承实验全寿命数据验证所提模型的有效性。 结果表明,PSO 算法优化 OCSVM 的模型可以准确地对轴承运行全寿命状态监
测,与支持向量描述(support vector data description, SVDD)和参数自选的 OCSVM 模型相比,该方法的性能退化评估模型更有效
和优越。 |
| 英文摘要: |
| In order to improve the accuracy of rolling bearing performance degradation assessment for one class support vector machine
(OCSVM), an ensemble empirical mode decomposition method based on adaptive white noise was proposed. A performance degradation
evaluation method combining CEEMDAN, particle swarm optimization ( PSO) and One class SVM. Firstly, CEEMDAN was used to
expand the collected vibration signal calculation into intrinsic mode functions ( IMFs), and typical characteristic signals were obtained
according to the IMFs energy density. Secondly, the parameters ν of OCSVM and radial basis kernel function g are optimized by particle
swarm optimization to enhance the learning ability and generalization ability of OCSVM. Finally, 3σ was used to set the adaptive
threshold, determine the early failure threshold of the bearing and verify the accuracy of the evaluation results by using the CEEMDAN
and Hilbert envelope demodulation method. The validity of the proposed model was verified by bearing experimental life data from the
University of Cincinnati. The results show that the PSO algorithm optimized OCSVM model can accurately monitor the bearing running
life state. Compared with the support vector data description ( SVDD) and parameter optional OCSVM model, the performance
degradation assessment model of this method is more effective and superior. |
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