Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

Abstract: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.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2607.26704 [stat.ML]

(or arXiv:2607.26704v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2607.26704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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