Defect detection by in-process monitoring plays a key role in the traceability and optimization of production. Many fault detection algorithms are trained on known faults. However, industrial data is generally unlabeled and certain faults are unknown or missing in the training dataset. This paper presents an original approach for the incremental discovery of new manufacturing defects, by Bayes rule and distance rejection. Rejects are analyzed periodically to determine the possible appearance of new defect cluster among them. Visualization then supports the cluster interpretation by a manufacturing expert. The approach was successfully applied to a screwing database from automotive industry.

通过过程监控进行缺陷检测在生产的可追溯性和优化中起着关键作用。许多故障检测算法都是针对已知故障进行训练的。然而，工业数据通常是未标记的，并且在训练数据集中某些故障是未知的或缺失的。本文提出了一种通过贝叶斯规则和距离拒绝增量发现新制造缺陷的原始方法。定期分析拒绝品以确定其中可能出现的新缺陷群。然后，可视化支持制造专家的集群解释。该方法已成功应用于汽车行业的拧紧数据库。