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

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 re

SUBMITTER: Neto JG 

PROVIDER: S-EPMC11815845 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature