控制对称与伍兹-萨克森随机共振启用了弱故障检测
Jian Liu1, Jiaqi Guo1, Bing Hu2
1College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的控制对称与伍兹-萨克逊静态共振 (CSwWSSR) 模型用于弱故障检测. CSwWSSR模型增强了信号与噪声比 (SNR) 以改善模拟和轴承数据中的故障诊断.
科学领域:
- 非线性信号处理是一种非线性信号处理.
- 随机共振应用 随机共振应用
- 检测故障的方法 检测故障的方法
背景情况:
- 传统的故障检测方法在较弱的信号下扎.
- 随机共振 (SR) 通过优化噪声利用,提供了增强的信号噪声比 (SNR).
- 现有的SR模型缺乏对潜在井定制的参数控制.
研究的目的:
- 开发一种新的控制对称与伍兹-萨克森静态共振 (CSwWSSR) 模型.
- 研究模型参数对潜在结构的影响.
- 为了优化CSwWSSR参数,使用粒子集群优化 (PSO) 进行卓越的故障检测.
主要方法:
- 开发CSwWSSR模型,一个三稳定的SR系统,具有独立控制的潜在井.
- 数学分析和实验验证对潜在结构的参数效应.
- 粒子群集优化 (PSO) 的应用用于参数调整.
- 使用模拟信号和实验性轴承数据进行故障诊断.
主要成果:
- 通过增强SNR,CSwWSSR模型展示了有效的弱故障检测.
- 数学分析澄清了个别参数对三稳定潜力景观的影响.
- 公共服务局成功确定了CSwWSSR模型的最佳参数.
- 实验结果证实了CSwWSSR模型对其组成模型的优越性.
结论:
- CSwWSSR模型为弱故障检测提供了强大的和可调节的方法.
- 控制的潜力井和优化的参数显著提高了诊断性能.
- 这种先进的SR技术为复杂的故障诊断场景提供了有希望的解决方案.
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