实现自适应和可解释的过程监控:增量变量图注意力自编码器与概率推理
IEEE transactions on cybernetics
|July 23, 2025
概括
本研究介绍了复杂工业系统的适应性和可解释性过程监测方法. 新的框架有效地处理非静止性,减少错误报警,提高监控可信度.
科学领域:
- 工业过程监控 工业过程监控
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 复杂的工业过程面临着非静态性挑战,如数据漂移和解释性问题.
- 现有的监测系统难以应对动态变化的数据,难以将新知识与旧知识协调.
- 值得信赖的流程监控需要适应波动的工业环境的适应性策略.
研究的目的:
- 开发一个适应性和可解释的过程监测框架,用于非静止的工业数据.
- 为了应对数据漂移,知识协调和流程监控中的解释性等挑战.
- 能够从动态的工业数据中持续学习,以可靠地检测故障.
主要方法:
- 使用贝叶斯规范化的自我组织地图进行故障区分的增量学习策略.
- 一个动态的下方采样重复策略,以防止在模型更新期间发生灾难性遗忘.
- 一个变量图注意力自编码器与概率推理用于可解释的空间表示学习.
- 适应值计算的增量变化贝叶斯推理.
- 异常感知图的注意力定位用于故障根源原因分析.
主要成果:
- 拟议的方法显著提高工业应用中的工艺监控性能.
- 与现有计划相比,错误报警率 (FAR) 显著降低.
- 该框架提供了检测到的故障之间的可解释的因果关系.
- 从动态变化的工业数据中有效地学习,保持监控准确度.
结论:
- 开发的增量变量图注意力自编码器框架为自适应和可解释的过程监控提供了强大的解决方案.
- 该方法成功地减轻了非静态工业数据带来的挑战,提高了监控可靠性.
- 这种方法通过提供清晰的故障定位和因果洞察,提高了工业过程监控的可靠性.
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