在高度动态的过程中跟踪恶化的因果发现的解释性
Asha Choudhary1, Matej Vuković1, Belgin Mutlu1
1Pro2Future GmbH, Inffeldgasse 25F, 8010 Graz, Austria.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了用于监测动态粘合纤维生产中的机械降解的因果发现. 可解释方法通过跟踪因果关系的变化随着时间的推移来识别过程问题.
科学领域:
- 工业工程 工业工程 工业工程
- 制造过程监控 制造过程监控
- 数据科学数据科学数据科学
背景情况:
- 机械降解对动态制造中的产品质量和效率产生重大影响.
- 粘纤维的生产是一个复杂的,动态的过程容易降解.
- 现有的异常检测方法缺乏解释性.
研究的目的:
- 开发一种新的,可解释的方法来监测粘性纤维生产中的机械降解.
- 利用因果发现技术来提高对降解机制的理解.
- 根据最先进的技术验证拟议的方法.
主要方法:
- 利用因果发现技术构建可解释的因果图.
- 在因果图构建中纳入域专业知识.
- 将该方法应用于粘性纤维生产的案例研究.
- 结果与基于LSTM的自动编码器,USAD和TranAD进行比较.
主要成果:
- 因果发现方法为降解监测提供了更好的解释性.
- 因果关系的变化有效地识别了生产过程中的潜在问题.
- 该方法证明了与现有的最先进技术的调整和验证.
- 这种方法在现实世界的粘性纤维生产环境中被证明是有效的.
结论:
- 因果发现提供了一个强大的,可解释的工具,用于监测动态制造中的降解.
- 拟议的方法增强了织行业的工艺优化和质量控制.
- 这项工作有助于推进异常检测和过程监控策略.
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