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

2026-07-29

AI Summary

This study addresses the challenge that traditional anomaly detection methods struggle to identify samples near the boundary between normal and anomalous states, thereby failing to enable early fault warnings. To overcome this limitation, the paper introduces the novel concept of “near-anomalies” and proposes CANARI, an unsupervised method grounded in Christoffel function theory to model such borderline cases. By moving beyond conventional dual-threshold mechanisms, CANARI proactively identifies unlabeled samples likely to evolve into failures. Experimental results on both synthetic and real-world industrial printed circuit board in-circuit test data demonstrate that CANARI significantly outperforms existing baselines, offering a robust foundation for predictive maintenance and quality control.

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.

Problem

• anomaly detection
• near-anomalies
• early failure prediction
• proactive approach
• predictive maintenance

Innovation

• near-anomaly detection
• Christoffel function
• unsupervised learning
• early failure prediction
• predictive maintenance