一种灵敏的频谱力辅助贝叶斯在线异常推断方法,用于轴承初始降解的动态检测
Renhe Yao1, Hongkai Jiang1, Yunpeng Liu1
1School of Civil Aviation, Northwestern Polytechnical University, 710072 Xi'an, China.
ISA transactions
|February 22, 2024
概括
这项研究引入了一种用于早期检测滚动轴承退化的新方法. 循环稳定性敏感频谱模糊力辅助贝叶斯在线异常推断 (CSFE-BOAI) 框架有效地识别初始故障,最大限度地减少虚假报警.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 在线状态监测和滚动轴承的预防性维护对于防止灾难性故障至关重要.
- 早期发现初始降解对于及时干预和避免严重事故至关重要.
研究的目的:
- 开发一个新的框架,CSFE-BOAI,用于敏感和强大的检测滚动轴承的初始降解.
- 提高轴承健康监测系统中异常检测的可靠性.
主要方法:
- 定义一个新的健康指数,循环稳定性敏感的光谱模糊 (CSFE),通过将模糊应用于循环稳定性敏感的光谱.
- 导出贝叶斯在线异常推断 (BOAI) 程序,使用连续CSFE数据的通用T分布.
- 使用Pauta标准和循环稳定性敏感频谱构建具有双异常确认的CSFE-BOAI框架.
主要成果:
- 对轴承降解数据集的实验验证证明了有效和及时的初始降解报警和识别.
- 与八种先进的健康指数和四种异常检测方法相比,CSFE-BOAI框架实现了最低的虚假和错误报警率.
- 拟议的方法显示出对干扰的稳定性和对初始降解的增强敏感性.
结论:
- CSFE-BOAI框架为滚动轴承的初始降解动态检测提供了可靠和准确的解决方案.
- 它在最大限度地减少虚假报警方面的卓越性能表明,它在工业应用中实际部署的巨大潜力.
- 这种方法推进了用于旋转机械的预测性维护的状态监测领域.
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