Learning operation strategies from alarm management systems by temporal pattern mining and deep learning

Gyula Dorgo, Peter Pigler, Mate Haragovics, J. Abonyi

Research output: Chapter

2 Citations (Scopus)

Abstract

We introduce a sequence to sequence deep learning algorithm to learn and predict sequences of process alarms and warnings. The proposed recurrent neural network model utilizes an encoder layer of Long Short-Term Memory (LSTM) units to map the input sequence of discrete events into a vector of fixed dimensionality, and a decoder LSTM layer to form a prediction of the sequence of future events. We demonstrate that the information extracted by this model from alarm log databases can be used to suppress alarms with low information content which reduces the operator workload. To generate easily reproducible results and stimulate the development of alarm management algorithms we define an alarm management benchmark problem based on the simulator of a vinyl acetate production technology. The results confirm that sequence to sequence learning is a useful tool in alarm rationalization and, in more general, for process engineers interested in predicting the occurrence of discrete events.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages1003-1008
Number of pages6
ISBN (Print)9780444642356
DOIs
Publication statusPublished - jan. 1 2018

Publication series

NameComputer Aided Chemical Engineering
Volume43
ISSN (Print)1570-7946

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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    Dorgo, G., Pigler, P., Haragovics, M., & Abonyi, J. (2018). Learning operation strategies from alarm management systems by temporal pattern mining and deep learning. In Computer Aided Chemical Engineering (pp. 1003-1008). (Computer Aided Chemical Engineering; Vol. 43). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-64235-6.50176-5