Extraction of qualitative behavior rules for industrial processes from reduced concept lattice

Article type: Research Article

Authors: Dias, Sérgio M.^a; * | Zárate, Luis E.^a | Song, Mark A.J.^a | Vieira, Newton J.^b | Kumar, Ch. Aswani^c

Affiliations: [a] Department of Computer Science, Pontifical Catholic University of Minas Gerais, Minas Gerais, Brazil | [b] Federal University of Minas Gerais, Minas Gerais, Brazil | [c] School of Information Technology and Engineering, VIT University, Vellore, India

Correspondence: [*] Corresponding author: Sérgio M. Dias, Department of Computer Science, Pontifical Catholic University of Minas Gerais, Minas Gerais, Brazil. E-mail: sergiomariano@gmail.com.

Abstract: Formal concept analysis (FCA) become an alternative approach to extract and represent knowledge of real world systems. That knowledge can be obtained from implications rules extracted of concept lattices formed by ordered formal concepts. However, in complex systems the number of formal concepts can be large. To deal with this complexity of the FCA, concept reduction techniques can be applied in order to balance the quality of information, and the computational cost for generating and handling the lattice. In this paper, we develop a novel approach to represent the behavior of physical processes through qualitative rules based on proper implications (minimum representation of the data) extracted from the reduced concept lattice. As a case study, the cold rolling process was considered. This process characterize by the strong non-linearity among its parameters. The results show that the qualitative behavior of the rolling process is preserved even when the reduction techniques are applied. The approach can be used to understand the relationship between process parameters through implication rules under different operating conditions of a process. The paper discusses some generic procedures that can be adapted to apply this approach to other industrial processes.

Keywords: Formal concept analysis, proper implications, cold rolling process

DOI: 10.3233/IDA-194569

Journal: Intelligent Data Analysis, vol. 24, no. 3, pp. 643-663, 2020