Methodologies based on data as useful tools to improve industrial processes

Resumen

In order to survive in an increasingly competitive world, industries try to improve their manufacturing processes in order to enhance the final quality of the product and save costs by means of an improved process control. The significance and relevance of optimizing the existing control models is even greater in the open-loop control systems or in those governed by computational methods dependent on adjustable parameters. During last decades, the gradual appearance of modem contro l systems in the industrial arena, makes possible the storage of an everyday increasing amount of information. Information reduces our uncertainty and, therefore, allows us to take decisions based on better criteria. Nevertheless, this massive amount of data implies a decrease in the easiness of interpretation. Fortunately, we can use Knowledge Discovery and Data Mining techniques as tools to reveal and extract useful knowledge from these massive data sets. This talk reviews some typical industrial environments and focuses on some parts of them in order to show the real interest of these improvements. We will identify some difficulties in obtaining these improvements and show how the optimal control model for the manufacturing process can be obtained from data provided by sensors. We will also discuss some technical problems that are related to the main goal, and will identify some topics concerning outliers, density and topology.