Title: Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets,
Abstract: This paper presents a model based on Deep Learning algorithms of LSTM and GRU
for facilitating an anomaly detection in Large Hadron Collider superconducting
magnets. We used high resolution data available in Post Mortem database to
train a set of models and chose the best possible set of their
hyper-parameters. Using Deep Learning approach allowed to examine a vast body
of data and extract the fragments which require further experts examination and
are regarded as anomalies. The presented method does not require tedious manual
threshold setting and operator attention at the stage of the system setup.
Instead, the automatic approach is proposed, which achieves according to our
experiments accuracy of 99%. This is reached for the largest dataset of 302 MB
and the following architecture of the network: single layer LSTM, 128 cells, 20
epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.
All the experiments were run on GPU Nvidia Tesla K80