Industrial Monitoring by Evolving Fuzzy Systems
Lara Aires, João Araújo, Antonio Carlos Dourado · European Society for Fuzzy Logic and Technology Conference · 2009
Industrial monitoring of complex processes with hundreds or thousands of variables is a hard task faced in this work through evolving fuzzy systems. The Visbreaker process of the Sines Oil Refinery is the case studied. Firstly dimension reduction is performed by multidimensional scaling, obtaining the process evolution in a three dimensional space. Then an evolving fuzzy system (eFS) is developed to detect eventual malfunction of sensors. This eFS takes the data in the three reduced dimensions as antecedents and classifies the process state into normal and abnormal states. A software platform- the eFSLab (Evolving Fuzzy Systems Laboratory) - , with which this work has been developed, is presented and discussed. Several strategies for rule creation and evolution of rules, for Takagi-Sugeno (and for Mamdani system obtained from these) , are implemented in eFSLab. The obtained eFS shows a promising performance in the case studied, classifying in some simulations the state of the process into abnormal-normal condition in about 95% of the cases, with a number of rules between 5 and 8.