  Modern radio telescopes combine thousands of receivers, long-distance
networks, large-scale compute hardware, and intricate software. Due to this
complexity, failures occur relatively frequently. In this work we propose novel
use of unsupervised deep learning to diagnose system health for modern radio
telescopes. The model is a convolutional Variational Autoencoder (VAE) that
enables the projection of the high dimensional time-frequency data to a
low-dimensional prescriptive space. Using this projection, telescope operators
are able to visually inspect failures thereby maintaining system health. We
have trained and evaluated the performance of the VAE quantitatively in
controlled experiments on simulated data from HERA. Moreover, we present a
qualitative assessment of the the model trained and tested on real LOFAR data.
Through the use of a naive SVM classifier on the projected synthesised data, we
show that there is a trade-off between the dimensionality of the projection and
the number of compounded features in a given spectrogram. The VAE and SVM
combination scores between 65% and 90% accuracy depending on the number of
features in a given input. Finally, we show the prototype
system-health-diagnostic web framework that integrates the evaluated model. The
system is currently undergoing testing at the ASTRON observatory.
