INDUSTRY 4.0: Predictive Intelligent Maintenance for Production Equipment
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In manufacturing, users increasingly demand comprehensive maintenance service in their production equipment in order to ensure high availability and to prevent downtimes in critical phases of the production processes, affecting customer delivery times. From the manufacturer’s point of view, it is vital to optimize and to improve the service provided to the final users, allowing appropriate maintenance planning and responding to the demand. Contrary to the classic preventive maintenance programs in use today, predictive maintenance improves the performance of the equipment, strengthening the business model of companies. Thanks to the inclusion of a set of sensing, condition monitoring, predictive analytics and distribute systems technologies, it is possible to perform and provide a remote technical assistance based on continuous monitoring and maintenance support from a distance. This paper shows the benefits and advantages to be achieved by the development of a comprehensive predictive maintenance, through the concept of Industry 4.0, and focuses on remote monitoring and self-diagnosis function of health condition for the equipment. However, the main emphasis of the work presents the data acquisition and analysis processes to develop predictive algorithms for machines in production.
How to Cite
##plugins.themes.bootstrap3.article.details##
PHM
Bonaldi, E.L.; Oliveira, L.E.L.; Borges da Silva, J.G.; Lambert-Torres, G and Borges da Silva, L.E. (2008). ―Detecting Load Failures using the Induction Motor as a Transducer‖, 10 th International Conference on Control, Automation, Robotics and Vision; Hanoi, Vietnam 2008 pp 196-199.
Jardine, A.K.S., Lin, D. and Banjevic, D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, Vol. 20,
Issue 7, pp. 1483-1510.
Kar, C. & Mohanty, A. R (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, vol. 20, pp. 158-187, 2006
Koch, V.; Kuge, S.; Geissbauer, R. & Schrauf, S (2014). 'Industry 4.0. Oportunities and challenges of the industrial Internet' Report, 2014.
Martin, K. F. (1994) A review by discussion of condition monitoring and fault diagnosis in machine tools. International Journal of Machine Tools and Manufacture, 1994, vol. 34, no 4, p. 527-551.
Medina-Oliva G., Voisin A., Monnin M., Peysson F., Leger JB. (2012). Prognostics Assessment Using Fleet-wide Ontology. PHM Conference 2012, Minneapolis, Minnesota, USA.
NILSSON, Nils (1998) J. Artificial intelligence: a new
synthesis. Morgan Kaufmann, 1998.
Power-OM Consortium (2012), Power consumption driven
Reliability, Operation and Maintenance optimisation,
www.power-om.eu
Rizzolo L., Abichou B., Voisin A., Kosayyer N. (2011),
Aggregation of Health Assessment Indicators of
Industrial Systems. In Proceedings of the 7th
conference of the European Society for Fuzzy Logic
and Technology, EUSFLAT-2011, Aix-Les-Bains,
France.
Robert B. Randall (2004), State of the Art in Monitoring
Rotating Machinery – Part 1 & 2, Sound and vibration,
2004
Saravanan, S., Yadava, G.S. and Rao, P.V. (2006)
Condition monitoring studies on spindle bearing of a
lathe, Advanced Manufacturing Technologies, pp. 993-
1005.
Wang, Wilson Q., Fathy Ismail, and M. Farid Golnaraghi
(2001). "Assessment of gear damage monitoring
techniques using vibration measurements." Mechanical
Systems and Signal Processing 15.5 (2001): 905-922.
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.