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Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine
Fault detection and diagnosis (FDD) in process industries is an important task for efficient process monitoring and plant safety. It is also essential for improving product quality and reducing production cost by reducing process downtime. Real-time multiscale classification of faults plays a vital role in process monitoring. However, some major issues such as high correlation, complexity, and nonlinearity of data are yet to be addressed. In this paper, a fault diagnosis approach based on multikernel support vector machines is proposed to classify the internal and external faults such as reflux failure, change in reboiler duty, column tray upsets, and change in feed composition, flow, and temperature in a distillation column. The data set is generated using Aspen plus dynamics simulation at normal and faulty states. The classification has been done by various methods such as decision tree, K-nearest neighbors, linear discriminant analysis, artificial neural network, subspace discriminant, and multikernel support vector machine. It is observed that the SVM based diagnostic system provides more accurate root cause isolation. The proposed MK-SVM method was evaluated by using the confusion matrix as the performance evaluator. The result showed that the proposed model has a high FDR which is 99.77 and a very low FAR, i.e., 0.23. © 2018 American Chemical Society.
Industrial and Engineering Chemistry Research
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