Article | Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden | Operations Dynamics of Gas Centrifugal Compressor: Process, Health and Performance Indicators Linköping University Electronic Press Conference Proceedings
Göm menyn

Title:
Operations Dynamics of Gas Centrifugal Compressor: Process, Health and Performance Indicators
Author:
Helge Nordal: University of Stavanger, Norway Idriss El-Thalji: University of Stavanger, Norway
DOI:
https://doi.org/10.3384/ecp20170229
Download:
Full text (pdf)
Year:
2019
Conference:
Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden
Issue:
170
Article no.:
035
Pages:
229-235
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2020-01-24
ISBN:
978-91-7929-897-5
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

Emerging technologies of Industry 4.0 have introduced novel ways of perceiving maintenance management, which has developed from being perceived as a “necessary evil” to become proactive with a holistic focusing on entire systems rather than single machines from Maintenance 3.0. In this context, the industry has begun to really appreciate the unique opportunities followed by system dynamics and simulation tool capabilities of representing the real world. However, maintenance management and performance are complex aspects of asset’s operation that is difficult to justify because of its multiple inherent trade-offs. Although the majority are unanimous when it comes to the expected impact maintenance plays on company profitability, this is in most cases challenging to determine and quantify. Moreover, relevant literature is considered as limited, especially with regard to impact simulation of Maintenance 4.0. Therefore, this paper focuses on the supportive function system dynamics, and modeling and simulation tools can be of help to assess behavior and predicting the future outcome of Maintenance 4.0 in the era of Industry 4.0. This includes developing a conceptualized model that enables simulating the future expected behavior i.e. (un)availability and cost by implementing such a maintenance system. In this context, a centrifugal compressor with the function of exporting gas to Europe is applied as a case study.

Keywords: Industry 4.0 architecture, system dynamics, maintenance management, impact simulation

Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden

Author:
Helge Nordal, Idriss El-Thalji
Title:
Operations Dynamics of Gas Centrifugal Compressor: Process, Health and Performance Indicators
DOI:
10.3384/ecp20170229
References:

I. Alsyouf. The role of maintenance in improving companies’ productivity and profitability. International Journal of Production Economics, 105(1): 70-78, 2007. 

D. Bardey. To maintain or not maintain? what should a risk-averse decision maker do?. J Qual Maint Eng, 11: 115-120, 2005. 

M. Bevilacqua and M. Braglia. The analytic hierarchy process applied to maintenance strategy selection. Reliab. Eng. Syst. Saf., 70(1): 71–83, 2000. 

M. Colledani, M. C. Magnanini and T. Tolio. Impact of opportunistic maintenance on manufacturing system performance. In CIRP Annals, 2018. 

U. Diwekar. Introduction to Applied Optimization. In Springer Science & Business Media. Berlin, Germany. 22, 2008. 

O.-E. V. Endrerud, J. P. Liyanage, and N. Keseric. Marine logistics decision support for operation and maintenance of offshore wind parks with a multi method simulation model. In Proceedings of the 2014 Winter Simulation Conference, 2014. 

A. Erguido, A. Crespo Márquez, E. Castellano and J. F. Gómez Fernández. A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renewable Energy, 114: 843-856, 2017. 

U. Gräber. Advanced maintenance strategies for power plant operators - introducing inter-plant life cycle management. In 29th MPA Seminar in the series Safety and Reliability of Pressure Components Stuttgart, Int. J. Press. Vessels Pip., 81(10–11): 861–865, 2004. 

T. Honkanen. Modelling Industrial Maintenance Systems and the Effects of Automatic Condition Monitoring. Master of Science, Helsinki University of Technology Information and Computer Systems in Automation, 2004.

Y.M. Hussain, S. Burrow, L. Henson, and P. Keogh. Benefits Analysis of Prognostics & Health Monitoring to Aircraft. Maintenance using System Dynamics. In EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY, 2016. 

M.S. Jalali, J. P. Kaiser, M. Siegel, and S. Madnick. The Internet of Things (IoT) Promises New Benefits – and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products. W. P. C. 2017-15, Cybersecurity Interdisciplinary Systems Laboratory (CISL), Sloan School of Management, Massachusetts Institute of Technology Cambridge, 2017. 

A.B. Jambekar. A systems thinking perspective of maintenance, operations, and process quality. Journal of Quality in Maintenance Engineering, 6(2): 123-132, 2000. 

T. Jokinen, P. Ylén, and J. Pyötsiä. Dynamic Model for Estimating the Added Value of Maintenance Services, Technical research centre of Finland, VTT, 2011. 

K. Komonen. A cost model of industrial maintenance for profitability analysis and benchmarking. Int. J. Production Economics, 79: 15-31, 2002. 

G. Linnéusson, A. Ng, and T. Aslam. Investigating Maintenance Performance: A Simulation Study. In 7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016. 

G., Linnéusson, A. H. C. Ng, and T. Aslam. Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation. Journal of Simulation 12(2): 171-189, 2018. 

G. Linnéusson, H. C. A. Ng, and T. Aslam. Towards strategic development of maintenance and its effects on production performance by using system dynamics in the automotive industry. International Journal of Production Economics, 200: 151-169, 2018. 

D. Maletic, M. Maletic, B. Al-Najjar, and B. Gomišcek. The role of maintenance regarding improving product quality and company’s profitability: A case study. IFAC Proceedings Volumes, 45(31): 7-12, 2012. 

J. Manyika, M. Chui, P. Bisson, J. Woetzel, R. Dobbs, J. Bughin, and D. Aharon. THE INTERNET OF THINGS: MAPPING THE VALUE BEYOND THE HYPE EXECUTIVE SUMMARY, McKinsey & Company, 2015. 

A. Markus, A. Marques, G. Kecskemeti, and A. Kertesz. Efficient Simulation of IoT Cloud Use Cases. Autonomous Control for a Reliable Internet of Services: 313-336, 2018. 

P. Marshall. System dynamics modeling of the impact of Internet-of-Things on intelligent urban transportation. In Regional Conference of the International Telecommunications Society (ITS), Los Angeles, CA, 25-28 October, 2015, International Telecommunications Society, Los Angeles. 

D.W. McKee, S. J. Clement, X. Ouyang, J. Xu, R. Romanoy and J. Davies. The Internet of Simulation, a Specialisation of the Internet of Things with Simulation and Workflow as a Service (SIM/WFaaS)). In 2017 IEEE Symposium on Service-Oriented System Engineering (SOSE), pages 47-56, 2017. 

R. Mobley. An Introduction to Predictive Maintenance. 2002. 

M. P. Noemi and L. William. Maintenance Scheduling: Issues, Results and Research Needs. International Journal of Operations & Production Management, 14: 47-69, 1994. 

T. D. Oesterreich and F. Teuteberg. Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83: 121-139, 2016. 

M. C. A. Olde Keizer, R. H. Teunter, J. Veldman and M. Z. Babai. Condition-based maintenance for systems with economic dependence and load sharing. In International Journal of Production Economics, 195: 319-327, 2018. 

L. Pintelon and A. Parodi-Herz. Maintenance: An Evolutionary Perspective. In Complex System Maintenance Handbook. Springer, London, Springer Series in Reliability Engineering, 2008. 

T. Qu, M. Thürer, J. Wang, Z. Wang, H. Fu, C. Li and G. Q. Huang. System dynamics analysis for an Internet-of-Things-enabled production logistics system. International Journal of Production Research, 55(9): 2622-2649, 2016. 

L. S. Register. Reducing the maintenance burden, 2017. Retrieved 20th of March, 2019, from https://www.offshoreenergytoday.com/reducing-the-maintenance-burden. 

L. Tan, J. Yang, Z. Cheng and B. Guo. Optimal replacement policy for cold standby system. Chin. J. Mech. Eng, 24 (2): 316–322, 2011. 

L. Wang, E. Zheng, Y. Li, B. Wang, J. Wu. Maintenance optimization of generating equipment based on a condition-based maintenance policy for multi-unit systems. In Chinese Control and Decision Conference (CCDC 2009), IEEE: 2440–2445, 2009. 

T. Wireman. World class maintenance management, 1990. 

T. Wireman. Benchmarking best practices in maintenance management. In Industrial Press, 2004. World Economic Forum. Digital Transformation Initiative Oil and Gas Industry, 2017. 

A. Zuashkiani, H. Rahmandad and A. K. S. Jardine. Mapping the dynamics of overall equipment effectiveness to enhance asset management practices. Journal of Quality in Maintenance Engineering 17(1): 74-92, 2011.

Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden

Author:
Helge Nordal, Idriss El-Thalji
Title:
Operations Dynamics of Gas Centrifugal Compressor: Process, Health and Performance Indicators
DOI:
https://doi.org10.3384/ecp20170229
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2019-11-06