基于相似性的剩余有用的寿命预测方法,考虑到认识学不确定性
Wenbo Wu1,2,3, Tianji Zou1,2,3, Lu Zhang1,2,3
1University of Chinese Academy of Sciences, Beijing 101408, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究引入了一种测量轨迹相似性的新方法,通过考虑采样不确定性来改善剩余使用寿命 (RUL) 预测. 这种新的方法提高了RUL预测在各种采样率的准确性和稳定性.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 剩余使用寿命 (RUL) 预测严重依赖于轨迹相似度.
- 现有的方法往往忽略了来自异步采样的认识不确定性或施加限制性假设.
- 这些局限性导致RUL分析中的结果偏差和预测精度降低.
研究的目的:
- 为降解轨迹开发一个强大的相似度衡量方法,以解释采样不确定性.
- 提高剩余使用寿命 (RUL) 预测模型的准确性和可靠性.
- 克服现有的相似度指标的局限性,如EDR,LCSS和DTW.
主要方法:
- 提出了一个不确定的圆模型来表示采样点作为不确定的分布的观测.
- 开发了一种新的相似度衡量指标,用于比较降解轨迹.
- 使用一个堆叠的拒绝自编码器 (SDA) 进行RUL预测,结合基于相似性的数据选择和微调.
主要成果:
- 拟议的相似性测量有效地模拟了采样不确定性,与传统方法不同.
- 在类似的降解数据上训练的SDA模型显示出优异的RUL预测性能.
- 与EDR,LCSS和DTW相比,新方法在不同的采样率中显示出更高的稳定性和准确性.
结论:
- 新的基于圆的不确定性相似度指标为轨迹比较提供了更准确和更强大的方法.
- 拟议的RUL预测框架,利用这个指标和SDA,显著超过现有方法.
- 这项工作为提高工程系统的预测和健康管理提供了一个有希望的方向.
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