Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems - Edoardo Storti; Laura Cattaneo; Adalberto Polenghi; Luca Fumagalli (Machines, 2018)
The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too.