Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

Although numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK-SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK-SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot-scale distillation column using Aspen Plus® is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK-SVM algorithm. The result showed that the proposed MK-SVM has a high fault detection rate (FDR) of 99.51 and a very low misclassification rate (MR) of 0.49. The MK-SVM�based classification is better with the F1 score of >97 for all combinations of faults. Moreover, it is observed that the proposed MK-SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine-learning algorithms. © 2022 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.