基于物理图的时空融合方法用于过程故障诊断
Fengzhen Zhang1, Qibing Jin1, Dazi Li1
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
ACS omega
|March 4, 2024
概括
这项研究引入了一种基于图形的新型模型,用于化学过程故障诊断,整合物理相关性和时空数据. 该方法实现了高精度,并为识别关键故障节点提供了可解释的解释.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 大数据和机器学习对于复杂的化学过程故障诊断至关重要.
- 现有的数据驱动方法往往忽视了物理系统的相关性,缺乏可解释性.
- 对于复杂的化学过程,需要一个强大且可解释的故障诊断框架.
研究的目的:
- 提出基于图形的故障诊断模型框架.
- 开发可靠的故障节点诊断分析方法,以提高可解释性.
- 提高化学过程中故障诊断的准确性和可靠性.
主要方法:
- 集成了一个图形卷积网络 (GCN) 用于空间特征提取和一个长短期内存 (LSTM) 网络用于时间依赖.
- 使用先验化学过程知识和皮尔森相关性来捕捉物理相关性,构建了相邻矩阵.
- 采用双重监督策略进行稳定的模型培训,并采用多模型投票策略进行可靠的推断.
- 开发了一种节点掩盖方法,用于可解释的故障节点分析.
主要成果:
- 拟议的模型在田纳西东曼工艺上的故障诊断中实现了高精度.
- 在所有故障类型中,平均故障诊断率达到0.9844%,证明了最先进的性能.
- 节点掩盖方法有效地识别了导致系统故障的关键节点,提高了可解释性.
结论:
- 基于图形的框架有效地整合了物理相关性和时空数据,用于准确的化学过程故障诊断.
- 提出的方法为复杂的工业系统提供了可靠和可解释的解决方案.
- 该模型在故障诊断准确性和可靠性方面取得了重大进展.
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