As radio telescopes increase in sensitivity and flexibility, so do their
complexity and data-rates. For this reason automated system health management
approaches are becoming increasingly critical to ensure nominal telescope
operations. We propose a new machine learning anomaly detection framework for
classifying both commonly occurring anomalies in radio telescopes as well as
detecting unknown rare anomalies that the system has potentially not yet seen.
To evaluate our method, we present a dataset consisting of 7050
autocorrelation-based spectrograms from the Low Frequency Array (LOFAR)
telescope and assign 10 different labels relating to the system-wide anomalies
from the perspective of telescope operators. This includes electronic failures,
miscalibration, solar storms, network and compute hardware errors among many
more. We demonstrate how a novel Self Supervised Learning (SSL) paradigm, that
utilises both context prediction and reconstruction losses, is effective in
learning normal behaviour of the LOFAR telescope. We present the Radio
Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based
anomaly detection and a supervised classification, thereby enabling both
classification of both commonly occurring anomalies and detection of unseen
anomalies. We demonstrate that our system is real-time in the context of the
LOFAR data processing pipeline, requiring <1ms to process a single spectrogram.
Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while
maintaining a false positive rate of ~2\%, as well as a mean per-class
classification F-2 score 0.89, outperforming other related works.

本文提出一种基于自监督学习的异常检测框架，应用于广泛的射电望远镜系统，能够在实时处理中，有效地分类常见异常及检测未知异常。通过为 Low Frequency Array（LOFAR）望远镜提供的数据集验证，该方法取得了高效且有效的结果，表现优于其他相关作品。