1. Fault classification in the process industry using polygon generation and deep learning.
- Author
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Elhefnawy, Mohamed, Ragab, Ahmed, and Ouali, Mohamed-Salah
- Subjects
INDUSTRY classification ,DEEP learning ,HAMILTONIAN graph theory ,POLYGONS ,PULP mills ,ARTIFICIAL intelligence ,FEATURE extraction - 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]
- Published
- 2022
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