Identification of relevant symptoms of performance degradation in industrial machines

In the last decades, manufacturing companies increasingly recognized the role of maintenance in guaranteeing high performances for their machines. At the same time, companies realized that, through the analysis of operational data, knowledge on the health status and performance of the machines could be generated, and maintenance-related optimizations and services could be offered to customers. In this setting, the identification of causes leading to degradation of key performance indicators (KPIs) of a machinery is of paramount importance in deciding what actions to take to improve machines performances. In this paper, we propose the use of symptomatology indicators that allow to automatically estimate symptoms of KPI decay in industrial machines. The effectiveness of the proposed symptomatology analysis is experimentally evaluated on real data coming from a set of four shrink wrappers, showing the benefits of the proposed indicators both on client and producer side.