一个基于图表的时间频率双流网络,用于工业过程中关键性能指标的多步预测
IEEE transactions on cybernetics
|September 4, 2024
概括
这项研究引入了一种基于图形的新型网络,用于工业过程中先进的多步预测,比目前的方法提高准确性. 该模型有效地捕捉了复杂的变量关系和长期依赖关系,以便更好地预测.
科学领域:
- 工业过程控制 工业过程控制
- 机器学习 机器学习
- 软传感器建模软传感器建模
背景情况:
- 深度学习软传感器模型主要专注于实时,当前步骤预测.
- 工业应用越来越需要对关键绩效指标进行先进的多步预测.
- 现有的方法在复杂的过程变量合和长期依赖性学习方面扎.
研究的目的:
- 开发一种先进的软传感器模型,用于准确的多步预测.
- 解决模拟复杂变量关系和长期依赖关系的局限性.
- 通过预测能力来加强工业过程的监测和控制.
主要方法:
- 提出了一个基于图表的时间频率双流网络,用于多步预测.
- 引入了多图的注意层,以模拟过程变量之间的动态合.
- 使用时频双流网络与多GAT来提取时间和频域特征.
- 实现了基于最小冗余和最大相关性的特征融合模块.
主要成果:
- 拟议的模型在现实世界的工业数据集上显著超过了最先进的方法.
- 在三步预测任务中,预测准确度 (RMSE,MAE,MAPE) 显著提高.
- 在废物焚烧数据集上实现了12.40%的RMSE,22.49%的MAE和21.98%的MAPE改进.
结论:
- 基于图的时间频率网络有效地处理复杂的变量合和长期依赖.
- 开发的模型为工业多步预测任务提供了卓越的性能.
- 这种方法为提高工业环境中的预测性维护和运营效率提供了有价值的工具.
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