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Fault classification in the process industry using polygon generation and deep learning.
- Source :
- Journal of Intelligent Manufacturing; Jun2022, Vol. 33 Issue 5, p1531-1544, 14p
- Publication Year :
- 2022
-
Abstract
- This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate "end-to-end learning" in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09565515
- Volume :
- 33
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Intelligent Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 156580078
- Full Text :
- https://doi.org/10.1007/s10845-021-01742-x