Fault classification in the process industry using polygon generation and deep learning

Mohamed Elhefnawy (), Ahmed Ragab () and Mohamed-Salah Ouali ()
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Mohamed Elhefnawy: Polytechnique Montréal
Ahmed Ragab: CanmetENERGY
Mohamed-Salah Ouali: Polytechnique Montréal

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 5, No 17, 1544 pages

Abstract: 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.

Keywords: Artificial intelligence (AI); Deep learning (DL); Convolutional neural network (CNN); Data visualization; Fault diagnosis; Hamiltonian cycles (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01742-x

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