Document Type: Original Article
Departamento de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, La Habana, Cuba
Departamento de Ciencias de Computos, Universidad de Puerto Rico, Recinto de Río Piedras, San Juan, Puerto Rico
Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, Brazil
Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, Brazil
In this paper, a hybrid algorithm using fuzzy clustering techniques is proposed for developing a robust fault diagnosis platform in industrial systems. The proposed algorithm is applied in a fault diagnosis scheme with online detection of novel faults and automatic learning. The hybrid algorithm identifies the outliers based on data density. Later, the outliers are removed, and the clustering process is performed. To extract the important features and improve the clustering, the maximum-entropy-regularized weighted fuzzy c-means is used. The use of a kernel function allows achieving a greater separability among the classes by reducing the classification errors. Finally, a step is used to optimize the parameters m (regulation factor of the fuzziness of the resulting partition) and (bandwidth, and indicator of the degree of smoothness of the Gaussian kernel function). The proposed hybrid algorithm was validated using the Tennessee Eastman (TE) process benchmark. The results obtained indicate the feasibility of the proposal.