4 papers
Discovery of fully efficient fault indicators along a data-based diagnosis process
Igor Bezmaternykh, Louise Travé-Massuyès, Elodie Chanthery
The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e…
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
Sena Ozgunay, Louise Travé-Massuyès, Louise Trav{é}-Massuy{è}s +2
Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in such systems is critical for…
Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
L{é}a Billet, Louise Trav{é}-Massuy{è}s, Elodie Chanthery +1
Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work int…
CLOE: Christoffel Loss Autoencoder for Anomaly Detection
Léa Billet, Louise Travé-Massuyès, Elodie Chanthery +1
Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightweight methods often struggle with high-dime…