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- Title
Fault classification in the process industry using polygon generation and deep learning.
- Authors
Elhefnawy, Mohamed; Ragab, Ahmed; Ouali, Mohamed-Salah
- 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.
- Subjects
CANADA; INDUSTRY classification; DEEP learning; HAMILTONIAN graph theory; POLYGONS; PULP mills; ARTIFICIAL intelligence; FEATURE extraction
- Publication
Journal of Intelligent Manufacturing, 2022, Vol 33, Issue 5, p1531
- ISSN
0956-5515
- Publication type
Article
- DOI
10.1007/s10845-021-01742-x