用自编码器+孤立森林检测加速器运行异常，提升对细微故障的识别能力。
Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX
- 先用自编码器重构传感器数据，再以重构误差输入孤立森林
- 在质子束流数据上验证，异常检测率显著优于传统方法
- 适合需要早期预警的高成本工业设备监测场景
ARRONAX的C70XP回旋加速器用于医疗与科研用放射性同位素生产，其复杂且昂贵的系统易发生故障，导致运行中断。本研究旨在基于机器学习方法，从时序传感器数据中实现早期异常检测，以提升系统性能。目前广泛使用的孤立森林（IF）虽具高效与可扩展性，但依赖轴平行分割，难以捕捉接近正常均值的细微异常。为此，本文提出一种混合方法：将全连接自编码器（AE）与孤立森林结合，利用AE重构后的传感器数据均方误差（MCE）作为IF模型输入。在质子束流强度时间序列数据上验证，该方法显著提升了检测性能，实验结果表明其对细微异常的识别能力明显增强。
原文摘要 · Abstract (English)
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 reconstructed by the AE is used as input to the IF model. Validated on proton beam intensity time series data, the proposed method demonstrates a clear improvement in detection performance, as confirmed by the experimental results.
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