A supervised cooperative clustering scheme for diagnosing process faults in an industrial plant
2016 IEEE Congress on Evolutionary Computation, CEC 2016
Classification, Cooperative clustering, Fault diagnosis, Genetic algorithm
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.
Anvaripour, Mohammad; Soltanpour, Sima; Razavi-Far, Roozbeh; Saif, Mehrdad; and Wu, Q. M.Jonathan. (2016). A supervised cooperative clustering scheme for diagnosing process faults in an industrial plant. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 160-167.