Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?

Journal: Journal of chemical information and modeling
Published Date:

Abstract

Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a single deep learning architecture type to assess whether a focused approach could enhance performance in fault diagnosis. We selected the benchmark Tennessee Eastman Process data set as our case study and investigated modifications on a reference convolutional neural network-based model. Results indicate a considerable improvement in the overall classification, reaching a maximum average F1-score of 89.85%, 7.47% above the baseline model, which is also a considerable improvement compared to other performances reported in the literature. These results emphasize the potential of this focused approach, indicating it could be further explored and applied to other data sets in future work.

Authors

  • João G Neto
    Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.
  • Karla Figueiredo
    Department of Informatics and Computer Science, Institute of Mathematics and Statistics, State University of Rio de Janeiro (UERJ); Rio de Janeiro, 20550-900, Brazil.
  • João B P Soares
    Department of Chemical Engineering, University of Alberta, 9211, 116 Street, Edmonton, Alberta T6G 1H9, Canada.
  • Amanda L T Brandão
    Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.