通过检测接近异常的样本，提前预警潜在故障，提升系统可靠性。
Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
- 基于基督弗尔函数，无监督识别接近异常的临界样本
- 在电路板测试数据上优于双阈值基线方法
- 适合需要预测性维护和质量控制的工业场景
异常检测在分布边界附近的样本上表现不确定，难以预判未来异常。本文提出‘近异常’概念——尚未构成异常但靠近边界、未来可能演变为异常的样本。为此，我们提出无监督方法CANARI，利用基督弗尔函数的理论基础实现近异常检测。该方法在印刷电路板的工业在线测试数据上验证，因真实标注数据缺失，采用合成近异常样本。实验表明，CANARI优于采用双阈值机制的基线方法，可有效提前发现异常，为系统韧性、预测性维护与质量控制提供前瞻性解决方案。
原文摘要 · Abstract (English)
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.
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