Fault classification in the process industry using polygon generation and deep learning
| Autor: | Mohamed Elhefnawy, Ahmed Ragab, Mohamed-Salah Ouali |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Interpretation (logic) business.industry Computer science Deep learning Feature extraction 02 engineering and technology Fault (power engineering) computer.software_genre Industrial and Manufacturing Engineering 020901 industrial engineering & automation Artificial Intelligence Polygon 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence Data pre-processing Data mining business Representation (mathematics) computer Software |
| Zdroj: | Journal of Intelligent Manufacturing. 33:1531-1544 |
| ISSN: | 1572-8145 0956-5515 |
| DOI: | 10.1007/s10845-021-01742-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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