Industry 4.0 for Aerospace Manufacturing: Condition Based Maintenance Methodology, Implementation and Challenges
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Industry 4.0 is the fourth industrial revolution where machine operations are linked through data communications and together, they form a production ecosystem. In Industry 4.0 settings, these operations are monitored, recorded and analyzed. This can be performed at Edge, Fog or Cloud levels.
In the industrial big data era, with ever maturing sensor technologies, data capture, communication and storage technologies, utilizing machine data for operational insights provides companies with competitive advantages. Benefits can include reduced operational and maintenance costs, a decrease in unscheduled downtime and greater assurance of on-time delivery of products. In this work, we will cover the milestones of implementing an industry 4.0 condition-based maintenance (CBM) strategy for machine tools and their surrounding systems. In addition, we will discuss the methodology for sensor selection, data collection, transmission and storage, return on investment for CBM and building CBM models for detection. Finally, will delve into challenges of implementing this methodology in industrial settings from both technology and logistics aspects.
How to Cite
##plugins.themes.bootstrap3.article.details##
Industry 4.0, Manufacturing, Condition Based Maintenance
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.