Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX
arXiv:2603.20335v1 Announce Type: new 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 reconstructed by the AE is used as input to the IF mo
arXiv:2603.20335v1 Announce Type: new 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 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.
Executive Summary
This article proposes a hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX. Building upon the limitations of Isolation Forest, the proposed method leverages a fully connected Autoencoder to enhance detection of subtle anomalies. By utilizing the Mean Cubic Error (MCE) of reconstructed sensor data as input to the Isolation Forest model, the authors demonstrate a significant improvement in detection performance. The method's efficacy is validated on proton beam intensity time series data, underscoring its potential for real-time anomaly detection in complex systems. The proposed approach has far-reaching implications for maintaining system reliability and efficiency in applications such as radioisotope production.
Key Points
- ▸ Hybrid Autoencoder-Isolation Forest approach for anomaly detection
- ▸ Enhanced detection of subtle anomalies using Mean Cubic Error (MCE)
- ▸ Validated on proton beam intensity time series data
Merits
Improved detection performance
The proposed method demonstrates a clear improvement in detection performance compared to traditional Isolation Forest approach.
Enhanced robustness
The use of Autoencoder to extract relevant features and enhance the representation of subtle anomalies improves the model's robustness to noise and outliers.
Demerits
Computational complexity
The additional computational overhead of training and integrating the Autoencoder may hinder the method's practical applicability in real-time systems.
Data quality requirements
The method's performance is heavily dependent on the quality of the input data, and poor data quality may compromise the model's accuracy.
Expert Commentary
This article constitutes a significant contribution to the field of industrial anomaly detection, particularly in the context of complex systems. By combining the strengths of Autoencoder and Isolation Forest, the authors offer a compelling solution for real-time anomaly detection. The method's adaptability to various industrial settings makes it a valuable addition to the existing toolkit. However, the computational complexity and data quality requirements must be carefully addressed to ensure practical applicability. Future studies should focus on evaluating the method's scalability and adaptability to diverse industrial settings.
Recommendations
- ✓ Further investigation into the method's scalability and adaptability to diverse industrial settings is necessary to fully realize its potential.
- ✓ Researchers should explore the application of the proposed hybrid approach to other domains, such as healthcare and finance, where anomaly detection is critical.
Sources
Original: arXiv - cs.LG
