  Daily operation of a large-scale experiment is a resource consuming task,
particularly from perspectives of routine data quality monitoring. Typically,
data comes from different sub-detectors and the global quality of data depends
on the combinatorial performance of each of them. In this paper, the problem of
identifying channels in which anomalies occurred is considered. We introduce a
generic deep learning model and prove that, under reasonable assumptions, the
model learns to identify 'channels' which are affected by an anomaly. Such
model could be used for data quality manager cross-check and assistance and
identifying good channels in anomalous data samples. The main novelty of the
method is that the model does not require ground truth labels for each channel,
only global flag is used. This effectively distinguishes the model from
classical classification methods. Being applied to CMS data collected in the
year 2010, this approach proves its ability to decompose anomaly by separate
channels.
