互动的多个模型估计器用于检测磁铁修复学阻尼器中的故障
Andrew Sanghyun Lee1, Yuandi Wu2, Stephen Andrew Gadsden2
1College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
一种新的故障检测估计器,即交互多模型扩展的滑动创新过器 (IMM-ESIF),在磁石学阻尼器中显著降低了80-90%的估计误差. 这种先进的方法使运行模式分类提高了4-5%,从而实现了可靠的自适应估计.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 非线性系统估计 非线性系统估计
背景情况:
- 交互多种模型 (IMM) 策略有效估计具有多种操作模式的系统.
- 基于模型的过器对于系统行为估计至关重要.
- 扩展卡尔曼波器 (EKF) 和无气味卡尔曼波器 (UKF) 是常见的,但具有模拟不确定性的局限性.
研究的目的:
- 提出和评估一种新的故障检测和诊断估计器:IMM-ESIF.
- 为了比较IMM-ESIF与IMM-UKF和其他非线性系统中的方法的性能.
- 在存在建模不确定性和混合操作条件的情况下评估估计器的稳定性.
主要方法:
- 实施了扩展的滑动创新过器 (ESIF),这是非线性系统的滑动创新过器的扩展.
- 将ESIF与IMM战略集成,以创建IMM-ESIF估计器.
- 在一个实验性的磁石学 (MR) 阻尼器设置上应用和比较IMM-ESIF与IMM-UKF.
主要成果:
- 与同行相比,IMM-ESIF的估计错误减少了80%至90%.
- 在正确分类操作模式方面实现了4%至5%的提升.
- 在混合操作条件和不确定性中展示了卓越的稳定性和准确性.
结论:
- IMM-ESIF是电机系统适应性估计的高效和高效的替代方案.
- 拟议的方法显著提高了复杂情景中估计的稳定性和效率.
- 在多种操作模式的系统中,IMM-ESIF显示出对故障检测和诊断的前景.
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