  Timely detected anomalies in the chemical technological processes, as well as
the earliest detection of the cause of the fault, significantly reduce the
production cost in the industrial factories. Data on the state of the
technological process and the operation of production equipment are received by
a large number of different sensors. To better predict the behavior of the
process and equipment, it is necessary not only to consider the behavior of the
signals in each sensor separately, but also to take into account their
correlation and hidden relationships with each other. Graph-based data
representation helps with this. The graph nodes can be represented as data from
the different sensors, and the edges can display the influence of these data on
each other. In this work, the possibility of applying graph neural networks to
the problem of fault diagnosis in a chemical process is studied. It was
proposed to construct a graph during the training of graph neural network. This
allows to train models on data where the dependencies between the sensors are
not known in advance. In this work, several methods for obtaining adjacency
matrices were considered, as well as their quality was studied. It has also
been proposed to use multiple adjacency matrices in one model. We showed
state-of-the-art performance on the fault diagnosis task with the Tennessee
Eastman Process dataset. The proposed graph neural networks outperformed the
results of recurrent neural networks.
