Paper Info
Title
Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories.
Abstract
Process output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more 'good parts' at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories.
Year
DOI
Venue
2018
10.1007/978-3-319-99707-0_35
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: SMART MANUFACTURING FOR INDUSTRY 4.0, APMS 2018
Keywords
Field
DocType
Industry 4.0,Predictive maintenance,Big data,Asset management,Smart factories,Sustainable manufacturing,Industrial production
Industrial production,Overall equipment effectiveness,Computer science,Manufacturing engineering,Profitability index,Production planning,Predictive maintenance,Industry 4.0,Big data,Asset management
Conference
Volume
ISSN
Citations 
536
1868-4238
0
PageRank 
References 
Authors
0.34
1
8
Name
Order
Citations
PageRank
Gökan May132.41
Nikos Kyriakoulis200.34
Konstantinos Apostolou300.34
Sangje Cho401.01
Konstantinos Grevenitis500.34
Stefanos Kokkorikos600.34
Jovana Milenkovic700.34
Dimitris Kiritsis842848.98