COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
COKE：结合时间顺序与专家知识的高缺失制造数据因果发现AI 解读 ↓
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COKE：结合时间顺序与专家知识的高缺失制造数据因果发现
- 研究问题
- 制造业传感器数据高维且大量缺失时，如何在不插补缺失值的情况下结合专家知识与记录时间顺序，学习机器间的因果关系？
- 方法与思路
- 作者利用工艺配方特征与可用样本，从含专家知识和时间顺序的初始图提取嵌入及传感器顺序图，再以 actor-critic 优化图生成。
- 材料陈述的贡献
- 摘要称，COKE 能同时利用缺失数据、专家知识和时间顺序，并在多种传感器规模、缺失比例及半导体数据评估中提高 F1 分数。
- 阅读关注点
- 应核对缺失机制假设、记录顺序与因果顺序的关系、奖励函数及图约束，并检查真实数据的参考图来源和基准比较是否公平。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核验；摘要中的性能增幅未经独立核验，也未说明缺失机制及因果图可识别的条件。
原文摘要
Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% missing data and high dimensionality from hundreds of sensors. These datasets also include domain-specific expert knowledge and chronological order information, reflecting the recording order across different machines, which is pivotal for discerning causal relationships within the manufacturing data. However, previous methods for handling missing data in scenarios akin to real-world conditions have not been able to effectively utilize expert knowledge. Conversely, prior methods that can incorporate expert knowledge struggle with datasets that exhibit missing values. Therefore, we propose COKE to construct causal graphs in manufacturing datasets by leveraging expert knowledge and chronological order among sensors without imputing missing data. Utilizing the characteristics of the recipe, we maximize the use of samples with missing values, derive embeddings from intersections with an initial graph that incorporates expert knowledge and chronological order, and create a sensor ordering graph. The graph-generating process has been optimized by an actor-critic architecture to obtain a final graph that has a maximum reward. Experimental evaluations in diverse settings of sensor quantities and missing proportions demonstrate that our approach compared with the benchmark methods shows an average improvement of 39.9% in the F1-score. Moreover, the F1-score improvement can reach 62.6% when considering the configuration similar to real-world datasets, and 85.0% in real-world semiconductor datasets. The source code is available at https://github.com/OuTingYun/COKE.