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数据模型交互式Rul预测随机降解装置的多重不确定性量化和多传感器信息融合
Caoyuan Gu1, Qi Wu2, Baokang Zhang2
1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China; Moganshan Institute ZJUT, Kangqian District, Deqing 313200, China.
ISA transactions
|January 9, 2025
概括
本研究引入了一种先进的方法,用于使用多传感器数据预测降解设备的剩余使用寿命 (RUL). 这种新的方法通过整合传感器数据和随机降解模型来提高RUL预测的准确性.
科学领域:
- 可靠性工程可靠性工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 准确的剩余使用寿命 (RUL) 预测对于随机降解装置的积极维护至关重要.
- 现有的方法经常与多源传感器数据和降解过程中固有的不确定性作斗争.
- 数据模型交互框架提供了潜力,但需要改进以改善RUL预测.
研究的目的:
- 为使用多源传感器的随机降解设备提出改进的RUL预测方法.
- 通过新的数据模型交互框架,提高RUL预测的准确性和可靠性.
- 解决传感器相互关系和降解建模中的三重不确定性的挑战.
主要方法:
- 利用k-最近邻居 (KNN) 来建立多源传感器之间的相互关系.
- 通过通过图形卷积网络 (GCN) 汇总传感器信息,构建了一个复合健康指数 (CHI).
- 开发了一种随机降解模型,在任何初始降解水平上考虑三倍的不确定性.
- 实现了一个数据模型交互机制,用于CHI和降解模型之间的闭环优化.
主要成果:
- 拟议的方法在RUL预测准确度方面取得了显著的改进.
- 与原始框架相比,对航空发动机和工具数据集的实验显示,全面性能至少提高了20%.
- 综合方法的有效性和优越性得到了验证.
结论:
- 开发的方法为随机降解系统的RUL预测提供了一种优越的方法.
- 通过GCN和精细的随机模型集成传感器数据显著提高了预测准确性.
- 数据模型交互机制为增强设备预测提供了一个强大的框架.
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