Ricerc@Sapienza

The present project proposal is part of a larger project that aims to implement maintenance strategies based on condition (condition-based maintenance) applied in the industrial sector. Consistently with the current national and production developments, the increasing attention to the acquisition of the process data at the machines, we want to build a model based on machine learning techniques to automatically identify the process phases of an industrial machinery, as well as highlight any abnormal behavior. In fact, several production systems/machine performs different processing phases and the process data are not automatically linked to the specific activity performed. The proposal stems from the industrial needs repeatedly collected by the research team, both by machine builders and users. As an example, the pharmaceutical production, particularly present in the Lazio with interest of Sapienza University, is based on production processes of granulation of the powders; this process is carried out inside a single machine that deals with preheating, conditioning, components addition, product rest, steam, etc., but collected data of the machine (e.g. outlet air temperature, or product humidity, etc.) has not the link to specific process phase. It should be emphasized in such contexts, that a predictive condition maintenance system leads to important cost reductions, avoiding production downtime or product loss, and minimizing the frequency of periodic maintenance interventions. The present research aims to use machine data available in the research group, acquired from real industrial contexts, to develop a machine learning system able to identify phases and anomalies, and a measure of criticality of the observed situation.