滚动轴承的双优化故障诊断方法基于分层斜坡透和SVM与鱼优化算法协同协同
Yuxing Li1,2, Bingzhao Tang1, Bo Huang1
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
一种新的方法,由白优化器 (WSO) 优化的等级斜率 (HSlopEn),改善了滚动轴承的故障诊断. 这种WSO-HSlopEn和WSO-SVM方法实现了高的识别率,在多个功能中达到100%.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 斜率 (SlopEn) 对于故障诊断是有效的,但在值选择方面存在困难.
- 现有的方法需要强大的值选择来准确识别故障.
研究的目的:
- 引入层次式斜率 (HSlopEn) 来增强故障诊断能力.
- 使用白优化器 (WSO) 开发双优化故障诊断方法,用于HSlopEn和支持矢量机 (SVM).
主要方法:
- 通过将层次结构纳入SlopEn.建议的等级倾斜 (HSlopEn) 通过将等级结构纳入SlopEn.
- 应用白优化器 (WSO) 来优化HSlopEn和SVM参数,创建WSO-HSlopEn和WSO-SVM.
- 测试了在滚动轴承故障诊断上使用单个和多个特征场景的双优化方法.
主要成果:
- 与其他等级输入相比,WSO-HSlopEn和WSO-SVM方法显示出更高的识别率.
- 在多功能条件下,识别率超过了97.5%,通过更多精选的功能得到改善.
- 在选择了五个节点时,实现了100%的最大识别率.
结论:
- 拟议的WSO-HSlopEn和WSO-SVM方法显著提高了滚动轴承故障诊断的准确性.
- 功能选择对诊断性能产生积极影响,使用五个节点实现最佳结果.
- 这种方法为复杂的故障诊断任务提供了强大而高性能的解决方案.
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