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

arXiv:2607.26704 · stat.ML, cs.LG · Submitted 2026-07-29 · Read on arXiv

L{é}a Billet, Louise Trav{é}-Massuy{è}s, Elodie Chanthery, Alexandre Gaffet

LAAS, INSA Toulouse · LAAS-DISCO, Comue de Toulouse · LAAS

stat.ML, cs.LG

Submitted: 2026-07-29

Journal ref: 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026), Sep 2026, Cork, Ireland

Code: https://github.com/fgrivet/CRISTAL

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Christoffel Function — 21× in this paper
• Core — 17× in this paper · explained in 2 episodes
• Latent Space — 7× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 6 episodes
• Ablation Study — 2× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Bill Of Materials — 2× in this paper
• False Negatives — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Predictive Maintenance — 2× in this paper · explained in 2 episodes
• Editor — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 4 episodes
• F1-score and Matthews Correlation Coefficient — 1× in this paper
• Mean Square Error — 1× in this paper
• Scoring Function — 1× in this paper · explained in 2 episodes

Sources

• Kernel-based Outlier Detection using the Inverse Christoffel Function
• Scalable anomaly detection via a univariate Christoffel function

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