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 adopted to apply this approach to other industrial processes.

中文翻译：

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从简化概念格中提取工业过程的定性行为规则

形式概念分析（FCA）成为提取和表示现实世界系统知识的替代方法。该知识可以从有序形式概念形成的概念格的蕴涵规则中提取。但是，在复杂的系统中，形式概念的数量可能很大。为了处理FCA的这种复杂性，可以应用概念简化技术，以平衡信息的质量以及生成和处理晶格的计算成本。在本文中，我们基于从简化概念格中提取的适当含义（数据的最小表示），开发了一种通过定性规则表示物理过程行为的新颖方法。作为案例研究，考虑了冷轧工艺。该过程的特征在于其参数之间的强非线性。结果表明，即使采用压下技术，轧制过程也能保持定性。该方法可用于通过过程不同操作条件下的蕴涵规则来理解过程参数之间的关系。本文讨论了一些通用程序，可以将这种方法应用于其他工业过程。