Enabling predictive analytics for smart manufacturing through an IIoT platform⁎This research leading has been partially funded by the European Commission under the H2020-IND-CE-2016-17 program, FOF-09-2017, Grant agreement no. 767561 "SERENA" project, VerSatilE plug-and-play platform enabling REmote predictive mainteNAnce. Issue 3 (2020)

• Record Type: Journal Article
• Authors: Cerquitelli, T.; Nikolakis, N.; Bethaz, P.; Panicucci, S.; Ventura, F.; Macii, E.; Andolina, S.; Marguglio, A.; Alexopoulos, K.; Petrali, P.; Pagani, A.; van Wilgen, P.; Ippolito, M.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 3(2020)
• Page Start: 179
• Page End: 184
• Publication Date: 2020
• Subjects: Data analytics -- data management -- analytics architecture -- predictive analytics -- production systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.11.029
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In the last few years, manufacturing systems are getting gradually transformed into smart factories. In this context, an increasing number of information and communication technologies is incorporated towards facilitating management, production, and control processes. The introduction of advanced embedded systems with enhanced connectivity produces a vast amount of data, posing a challenge in terms of data analytics. However, the in-time collection and analysis of acquired data can create insight into the manufacturing process as well as its assets. One aspect of major importance for every production system is preserving its equipment in operational condition, and within those limits that could minimize unplanned breakdowns and production stoppages. This paper details the predictive analytics methodology integrated into the SERENA platform able to: (i) streamline the prognostics of the industrial components, (ii) characterize the health status of the monitored equipment, (iii) generate an early warning related to the condition of the equipment, and (iv) forecast the future evolution of the monitored equipment's degradation. To demonstrate the effectiveness of the proposed methodology, different use cases are discussed with results obtained on real-data collected in real-time from the industrial environments.