UPTIME Predictive Maintenance:
Lessons Learned and Best Practices in White Goods Industry
The UPTIME 1st live webinar highlighted the differences between classic preventive maintenance and predictive maintenance using historical data and real time data as well as illustrated benefits of predictive maintenance by a concrete implementation in the White Goods Industry.
The UPTIME white goods business case deals with Whirlpool’s complex automatic production line, which produces drums for clothes dryers. The business case aims to anticipate planned intervention on machines, to reduce unexpected breakdowns and delay other interventions, thus save money and improve safety. Moreover, three main important lessons learned as well as best practices for manufacturing companies to get started with predictive maintenance were presented.
The webinar is free of charge, dedicated to people who want to learn and see a concrete implementation of the UPTIME Predictive Maintenance Platform in a real business case. It is interactive, where you have the opportunity to ask questions to the experts panel and we are happy to receive your feedback.
If you have any questions or comments, please contact us.
Pierluigi Petrali has been working at Whirlpool Europe since 1989. He started his career in Information Technology, working at introduction in Whirlpool of CIM, MES and Fault Tolerant Networks. He has then worked in innovation projects, contributing to the spread and growth of TRIZ methodology in Italy and Europe and to the development of product/process design support approaches.
Presently he coordinates manufacturing research and development activities for EMEA region. Directly involved as technical and main partner in more than 10 funded research projects under FP7 and H2020 program in the last five years, he is currently working in definition and implementation of Whirlpool Industry 4.0 strategy.
He is the inventor of five patents and author several technical papers and public speech on TRIZ, Technology Forecasting, Manufacturing Quality, Internet of Things and Industry 4.0.
Graduated in Electronic Engineering at the University of Genoa, Mr. Barbieri joined RINA (formerly D’Appolonia) in 2001 and has participated to several projects at National and International Levels managing and supporting the entire “software life cycle”, from Software specification to software maintenance, passing through the Design, development and testing.
Mr Barbieri has been involved in several safety and security projects for risk analysis, specification of requirements and design of countermeasures and mechanisms for hardware and software processing sensitive data. Mr Barbieri was involved as technical coordinator in several EC and industrial projects.
In particular, in the framework of UPTIME project Mr Barbieri acts as Technical Coordinator for RINA following the development of Failure Mode and Effect Criticality Analysis (FMECA) Tool, the validation of the UPTIME Platform and the conduction of FMECA Workshop with the Three Business Case: Whirlpool, Maillis and FFT.
Fenareti Lampathaki holds a Ph.D. Degree in Information Systems’ Semantic Interoperability and a Diploma – M.Eng. Degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), as well as an MBA Degree in Techno-Economics.
She is the Technical Director and among the co-founders of Suite5 Data Intelligence Solutions Limited. During the last 15 years, she has successfully led the team’s research and management activities in a series of EU-funded R&D projects in multiple domains (e.g. Manufacturing, Energy, Aviation) related to data interoperability, big data analytics and AI.
Her research results have appeared in over 75 publications in international journals, edited books and conference proceedings while she has co-edited 1 book. Finally, she has been serving as a reviewer for R&D projects and evaluator for the European Commission since 2012, as well as a peer reviewer in academic journals and conferences.