强大的基于HMM的剩余使用寿命估计,使用度调整的EM算法
Halime Beyza Küçükdağ1, Gokhan Kirkil1, Mustafa Hekimoğlu2
1Department of Computational Applied Science and Engineering, Kadir Has University, Istanbul 34083, Turkey.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一个强大的框架,用于使用隐藏的马尔科夫模型 (HMM) 预测工程系统的剩余使用寿命 (RUL). 这种新的方法提高了关键机械预测的准确性和可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 统计 统计 统计 统计
- 机器学习 机器学习
背景情况:
- 估计剩余使用寿命 (RUL) 对于维护规划和确保复杂机械系统的可靠性至关重要.
- 准确的RUL预测对于防止意外故障和实现及时干预至关重要.
研究的目的:
- 开发一个统计学上可靠的框架来建模系统退化和预测RUL.
- 提高工程系统中RUL估计的准确性和可靠性.
主要方法:
- 使用了一个隐藏的马尔科夫模型 (HMM),具有简单的故障结构和吸收终端状态.
- 在参数估计中采用了度规范的预期最大化 (EM) 算法,并采用了基于Huber的规模估计器.
- 计算RUL作为一个加权的吸收预期时间,使用前向后向算法来平滑后向状态概率.
主要成果:
- 与基线WLS-EM相比,拟议的度规范化EM算法显示了显著减少的参数方差.
- 在模拟和真实数据分析中实现了更好的预测准确性和更流,更可靠的RUL预测轨迹.
- 该框架提供了一个低方差,状态意识的RUL估计器,保留了HMM的概率结构.
结论:
- 开发的框架为实际的预测应用提供了一个强大的和可解释的方法.
- 该方法有效地模拟了降解信号,并提高了RUL估计的准确性.
- 这种统计学上合理的方法适用于维护复杂的工程系统.
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