Fuzzy Classification of Industrial Data for Supervision of a Dewatering Machine: Implementation Details and Results

Abstract

In this document, real data collected in an industrial process are studied and analyzed, with the intention of improving the process supervision seeking for operational efficiency and saving resources, emphasizing in the information cleaning process using basic statistics and data analysis based on non-supervised clustering algorithms: Lamda, GK means and Fuzzy C-means. A general data cleaning procedure for use in industrial environments is suggested. The procedure proposed is followed in a case for a centrifuge machine for mud treatment, three versions of fuzzy classifiers were tested where fuzzy, c-means was finally selected and a result is obtained that permits detecting an inefficient operating state, in some cases the machine was running at a normal current and spending energy and other resources for a long period and the mud was not treated properly, the exit mud was practically the same as the mud at the entrance. The trained classifier has been implemented directly in the PLC used to control the machine, and the results of online classification have been verified showing that states correspond with the process behavior.