A supervised cooperative clustering scheme for diagnosing process faults in an industrial plant

Document Type

Conference Proceeding

Publication Date

11-14-2016

Publication Title

2016 IEEE Congress on Evolutionary Computation, CEC 2016

First Page

160

Keywords

Classification, Cooperative clustering, Fault diagnosis, Genetic algorithm

Last Page

167

Abstract

This paper presents a novel supervised clustering technique including different clustering algorithms which cooperate together to span the decision space in a supervised manner. It uses a variety of clustering methods for an efficient partitioning. An evolutionary algorithm is used to tune the key parameters of the cooperative scheme which minimizes an error-based objective function on the training dataset. The proposed supervised scheme is developed for diagnosing faults in the Tennessee Eastman process, which is a standard benchmark for fault detection and diagnosis. Experimental results show that the proposed technique can efficiently diagnose the process faults.

DOI

10.1109/CEC.2016.7743791

ISBN

9781509006229

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