Resumen

BERNAL-DE LAZARO, José M.; PRIETO-MORENO, Alberto; LLANES-SANTIAGO, Orestes  y  GARCIA-MORENO, Emilio. A comparative study of clasification methods used in the fault diagnosis of industrial systems. Ingeniería Mecánica [online]. 2011, vol.14, n.2, pp. 87-98. ISSN 1815-5944.

This paper, presents a comparative study of the performance of four classification techniques very used in fault diagnosis of industrial processes. The selected techniques were: k-Nearest neighbor (k-NN), Partial least-squares (PLS), Artificial Neuronal Networks (ANN) and Support Vector Machines (SVM). The comparison is based in the classification capacity of the historical data and the generalization using new observations. The four techniques are applied to historical data of the known benchmark Tennessee Eastman industrial process. The comparison permitted to prove as the generalization capacity of the classification techniques grow with the complexity of classifiers without to increase the computational effort in the fault diagnosis.

Palabras clave : industrial process; fault diagnosis; industrial maintenance; support vector machines; artificial neural networks; partial least-squares; k-nearest neighbor method.

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