2017 |
Derbali, M.; Buhari, S. M.; Tsaramirsis, G.; Stojmenovic, M.; Jerbi, H.; Abdelkrim, M. N.; Al-Beirutty, M. H. Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study Article de journal Dans: IEEE Access, vol. 5, p. 23266-23275, 2017, ISSN: 21693536, (cited By 17). Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence; Data mining; Decision trees; Deep learning; Desalination; Education; Failure analysis; Feature extraction; Learning algorithms; Learning systems; Principal component analysis; Regression analysis; Trees (mathematics); Valves (mechanical); Water filtration, Class imbalance problems; Comparative studies; Independent variables; Machine learning approaches; Machine learning techniques; Regression techniques; Stepwise regression; Water desalination, Fault detection @article{Derbali201723266,The presence of faulty valves has been studied in the literature with various machine learning approaches. The impact of using fault data only to train the system could solve the class imbalance problem in the machine learning approach. The data sets used for fault detection contain many independent variables, where the salient ones were selected using stepwise regression and applied to various machine learning techniques. A significant test for the given regression technique was used to validate the outcome. Machine learning techniques, such as decision trees and deep learning, are applied to the given data and the results reveal that the decision tree was able to obtain more than 95% accuracy and performed better than other algorithms when considering the tradeoff between the processing time and accuracy. © 2013 IEEE. |
Publications
2017 |
Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study Article de journal Dans: IEEE Access, vol. 5, p. 23266-23275, 2017, ISSN: 21693536, (cited By 17). |