局部-全球相关性基于融合的图形神经网络,用于预测剩余的有用寿命
IEEE transactions on neural networks and learning systems
|November 20, 2023
概括
这项研究引入了通过有效建模传感器相关性来预测剩余使用寿命 (RUL) 的新框架. 该LOGO方法融合了本地和全球传感器数据,改善了预后和健康管理.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 剩余使用寿命 (RUL) 预测对于系统预后和健康管理至关重要.
- 深度学习 (DL) 模型擅长捕获RUL预测的时间序列数据中的时间依赖性.
- 现有的方法难以从多传感器数据中建模空间依赖性,从而限制了RUL预测的准确性.
研究的目的:
- 提出一个新的框架,LOGO (LOcal-GlObal相关性融合),用于增强RUL预测.
- 为了有效地建模和捕捉本地和全球传感器相关性.
- 通过解决空间依赖模型中的局限性来提高RUL预测的准确性.
主要方法:
- 开发了一个局部-全球相关性融合 (LOGO) 框架.
- 包含了局部相关性 (动态传感器关系) 和全球相关性 (稳定传感器关系).
- 利用自适应融合机制结合相关性和定义序列微图用于图形神经网络 (GNN) 分析.
主要成果:
- 通过融合本地和全球传感器相关性,LOGO框架有效地建模空间依赖.
- 图形神经网络在微图中捕获空间依赖性,而时间依赖性则被顺序捕获.
- 实验结果表明,拟议的方法对准确的RUL预测的有效性.
结论:
- 通过有效地整合本地和全球传感器相关信息,LOGO框架在RUL预测方面取得了重大进展.
- 拟议的方法增强了预测和健康管理系统的特征学习.
- 该方法为使用多传感器数据预测系统的剩余使用寿命提供了更强大的解决方案.
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