Paper Info
Title
Integrated Intelligent Green Scheduling of Predictive Maintenance for Complex Equipment based on Information Services.
Abstract
As an important link to guarantee normal industrial production, equipment maintenance plays an increasingly key role in enhancing the competitiveness of enterprises and supporting green smart manufacturing. This paper aims to promote the implementation of predictive maintenance for complex equipment and improve the green performance of the maintenance service process. A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service. Subsequently, an integrated mathematical problem model that consists of carbon emission objective and extended maintenance cost objective is constructed. Then an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem. In response to deal with the uncertainties of maintenance service environment and inaccuracy of prediction, a data-driven dynamic adjustment strategy is applied. A grinding roll fault case of a large vertical is used to demonstrate the effectiveness of this proposed approach.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2977667
IEEE ACCESS
Keywords
DocType
Volume
Predictive maintenance,Job shop scheduling,Optimization,Green products,Predictive maintenance,complex equipment,green manufacturing,information service network,integrated multiobjective optimization
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Shanghua Mi100.34
Yixiong Feng27813.99
Hao Zheng316833.81
Zhi Wu Li447038.43
Yicong Gao5142.93
Tan Jianrong66421.62