The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An architecture for fault detection and isolation based on fuzzy methods
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Support Vector Machine (SVM) as an innovative machine learning tool, based on statistical learning theory, is recently used in process fault diagnosis tasks. In the application of SVM to a fault diagnosis problem, typically a discrete decision function with discrete output values is utilized in order to solely define the label of the fault. However, for incipient faults in which fault steadily progresses over time and there is a changeover from normal operation to faulty operation, using discrete decision function does not reveal any evidence about the progress and depth of the fault. Numerous process faults, such as the reactor fouling and degradation of catalyst, progress slowly and can be categorized as incipient faults. In this work a continuous decision function is anticipated. The decision function values not only define the fault label, but also give qualitative evidence about the depth of the fault. The suggested method is applied to incipient fault diagnosis of a continuous binary mixture distillation column and the result proves the practicability of the proposed approach. In incipient fault diagnosis tasks, the proposed approach outperformed some of the conventional techniques. Moreover, the performance of the proposed approach is better than typical discrete based classification techniques employing some monitoring indexes such as the false alarm rate, detection time and diagnosis time.