发表机构
Schaeffler; LAAS-CNRS; University of Toulouse; INSA; CNRS（舍弗勒; 法国国家科学研究中心自动化与系统分析实验室; 图卢兹大学; 法国国立应用科学学院; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出CANARI无监督方法，利用克里斯托费尔函数检测近异常，在印刷电路板工业测试数据上验证，性能优于双阈值基线，可主动预测故障，助力弹性维护与质量控制。
AI 中文摘要
异常检测方法对于分布边界附近的样本往往表现出不确定的行为，这限制了它们预测未来异常的能力。本研究引入了近异常的概念，这类样本虽尚未成为异常，但靠近边界且极有可能在近期转变为异常。为解决该问题，我们提出了一种名为基于克里斯托费尔函数的早期发现异常预测方法（Christoffel-based ANomaly Anticipation for eaRly dIscovery，CANARI）的无监督方法，该方法利用克里斯托费尔函数的强大理论基础来检测近异常。我们在印刷电路板的工业在线测试数据上对该方法进行了验证，由于缺乏真实世界的标注数据，我们生成了合成的近异常样本。实验结果表明，CANARI的性能优于所对比的基线方法，这些基线方法通常采用双阈值机制（一个用于异常，一个用于近异常）。因此，CANARI为在异常发生前进行预测提供了一种主动解决方案，为弹性、预测性维护和质量控制提供了一种有前景的方法。
英文摘要
Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work introduces the concept of near-anomalies that, while not yet anomalous, lie close to the boundary and are likely to transition into anomalies in the near future. To address this, we propose an unsupervised method, named Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI), which leverages the strong theoretical foundations of the Christoffel function to detect near-anomalies. The method is validated on industrial in-circuit testing data from printed circuit boards, with synthetically generated near-anomaly samples due to the lack of real-world data labeling. Experimental results show that CANARI outperforms the compared baselines that generally use a dual-threshold mechanism (one for anomalies and one for near-anomalies). It therefore provides a proactive solution for anticipating anomalies before they occur, offering a promising approach for resilience, predictive maintenance, and quality control.
Journal ref37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026), Sep 2026, Cork, Ireland
