Cognitive maintenance for high-end equipment and manufacturing
- ,
- Kesheng Wang,
- Guohong Dai
- ,
- ,
- University of Plymouth,
- Norwegian University of Science and Technology,
- Changzhou University
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review
Abstract
Traditionally, In order to predict impending failures and mitigate downtime in their manufacturing facilities, we have to combine many techniques, both quantitative and qualitative, such as smart sensors, high-end intelligent equipment, smart networks, Internet of Thing (IOT), Artificial Intelligence (AI), business analysis decision-making and Internet of service IOS. Based on Industry 4.0 concept, Cognitive Maintenance (CM) or Intelligent Predictive Maintenance (IPdM) systems, which uses intelligent data analysis and decision making techniques, offers the maintenance professionals in high-end equipment the potential to optimize maintenance tasks in real time, maximizing the useful life of their equipment and manufacturing assets while still avoiding disruption to operations. In this paper, we will present the impact of CM to high-end equipment, the framework of Cognitive Maintenance (CM) system and a case study. Some lessons learned from the implementation of CM system in industry are discussed.
Publication Information
Output type
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review
Original language
EnglishArticle number
Chapter 49Pages from-to (Number of pages)
Pages 394-400Publication milestones
- Published - 15/12/2018
Publication status
Published - 15/12/2018
Publisher
Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, SingaporePublication series
- Publication series name: Advanced Manufacturing and Automation VIII
ISSN (Print): 1876-1100
ISSN (Electronic): 1876-1119
Volume: 484
ISBN (Print)
9789811323744ISBN (Electronic)
9789811323751External Publication IDs
- Scopus: 85059077232
