用机器学习从喷头喷嘴日志中识别故障模式，提升打印质量检测效率。
Machine Learning for Pattern Detection in Printhead Nozzle Logging
- 基于时间与空间特征，构建喷嘴行为模式的分类模型。
- 随机森林在多个故障类型上达到最高加权F1分数。
- 相比传统规则系统，对复杂故障模式识别更准确，适合工业质检场景。
正确识别故障机制对制造商确保产品品质至关重要。佳能生产打印技术开发的某些喷头故障可通过单个喷嘴的行为识别，这些状态持续记录并形成随时间与喷嘴阵列空间分布的特定模式。本文研究基于多维度喷嘴日志数据的喷头故障分类问题，提出一种机器学习分类方法。采用基于特征的时间序列分类框架，由领域专家指导选取时间与空间特征。评估多种传统机器学习分类器后，发现一对多随机森林表现最佳。所提模型在多个故障机制上的加权F1分数优于内部规则基线。
原文摘要 · Abstract (English)
Correct identification of failure mechanisms is essential for manufacturers to ensure the quality of their products. Certain failures of printheads developed by Canon Production Printing can be identified from the behavior of individual nozzles, the states of which are constantly recorded and can form distinct patterns in terms of the number of failed nozzles over time, and in space in the nozzle grid. In our work, we investigate the problem of printhead failure classification based on a multifaceted dataset of nozzle logging and propose a Machine Learning classification approach for this problem. We follow the feature-based framework of time-series classification, where a set of time-based and spatial features was selected with the guidance of domain experts. Several traditional ML classifiers were evaluated, and the One-vs-Rest Random Forest was found to have the best performance. The proposed model outperformed an in-house rule-based baseline in terms of a weighted F1 score for several failure mechanisms.
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