基于数字双胞胎模型的滚动轴承故障的新型定量诊断方法
Lingli Cui1, Wenjie Li1, Xin Wang2
1Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.
ISA transactions
|December 31, 2024
概括
这项研究引入了一种新的数字双胞胎模型,用于准确诊断滚动轴承故障. 该方法有效地从噪声信号中提取故障信息,提高诊断准确性和可靠性.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 信号处理 信号处理
背景情况:
- 滚动轴承的定量诊断依赖于分析双冲动行为.
- 从噪音信号中提取精确的故障时刻仍然是一个重大挑战.
研究的目的:
- 提出一种使用数字双胞胎模型评估轴承故障严重性的新型定量诊断方法.
- 准确地从噪声污染的原始信号中提取故障信息.
主要方法:
- 对轴承故障的定量诊断标准的推导,以分析瞬时响应特征.
- 构建一个数字双胞胎模型,以使用无噪声双胞胎信号来描述故障信号.
- 双胞胎模型的实时优化,使用基于等号相似性的递归参数优化策略 (RPOS-CS).
- 从最佳信号应用故障参数来评估轴承故障大小.
- 利用核心密度估计用于不确定性分析和间隔估计.
主要成果:
- 拟议的数字双胞胎方法准确地评估来自原始信号波形的轴承故障严重性.
- 该RPOS-CS策略有效地优化双胞胎模型实时.
- 核密度估计通过不确定性分析提高了诊断结果的可靠性.
- 使用模拟和实验信号的验证证明了高的诊断准确性和可靠性.
结论:
- 这种新的基于数字双胞胎的方法提供了一种可靠的方法,用于定量诊断滚动轴承故障.
- 该技术有效地克服了与噪声污染信号相关的挑战.
- 该研究强调了改进机械状况监测和预测性维护的潜力.
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