[2603.20335] Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX

Computer Science > Machine Learning arXiv:2603.20335 (cs) [Submitted on 20 Mar 2026] Title:Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX Authors:F Basbous (Nantes Univ, GIP ARRONAX), F Poirier (GIP ARRONAX, CNRS), F Haddad (GIP ARRONAX, Nantes Univ, CNRS), D Mateus (Nantes Univ - ECN, LS2N) View a PDF of the paper titled Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX, by F Basbous (Nantes Univ and 8 other authors View PDF Abstract:The Interest Public Group ARRONAX's C70XP cyclotron, used for radioisotope production for medical and research applications, relies on complex and costly systems that are prone to failures, leading to operational disruptions. In this context, this study aims to develop a machine learning-based method for early anomaly detection, from sensor measurements over a temporal window, to enhance system performance. One of the most widely recognized methods for anomaly detection is Isolation Forest (IF), known for its effectiveness and scalability. However, its reliance on axis-parallel splits limits its ability to detect subtle anomalies, especially those occurring near the mean of normal data. This study proposes a hybrid approach that combines a fully connected Autoencoder (AE) with IF to enhance the detection of subtle anomalies. In particular, the Mean Cubic Error (MCE) of the sensor data reconstru...