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Fault classification in the process industry using polygon generation and deep learning
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DOI：10.1007/s10845-021-01742-x.png)
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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.
Keyword:
Artificial intelligence (AI)
Deep learning (DL)
Convolutional neural network (CNN)
Data visualization
Fault diagnosis
Hamiltonian cycles
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3.5K
被引数:
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