  Alarm management systems have become indispensable in modern industry. Alarms
inform the operator of abnormal situations, particularly in the case of
equipment failures. Due to the interconnections between various parts of the
system, each fault can affect other sections of the system operating normally.
As a result, the fault propagates through faultless devices, increasing the
number of alarms. Hence, the timely detection of the major fault that triggered
the alarm by the operator can prevent the following consequences. However, due
to the complexity of the system, it is often impossible to find precise
relations between the underlying fault and the alarms. As a result, the
operator needs support to make an appropriate decision immediately. Modeling
alarms based on the historical alarm data can assist the operator in
determining the root cause of the alarm. This research aims to model the
relations between industrial alarms using historical alarm data in the
database. Firstly, alarm data is collected, and alarm tags are sequenced. Then,
these sequences are converted to numerical vectors using word embedding. Next,
a self-attention-based BiLSTM-CNN classifier is used to learn the structure and
relevance between historical alarm data. After training the model, this model
is used for online fault detection. Finally, as a case study, the proposed
model is implemented in the well-known Tennessee Eastman process, and the
results are presented.
