A Methodology for Fast Deployment of Condition Monitoring and Generic Services Platform Technological Design

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Published Jul 5, 2016
Santiago Fernandez Christophe Mozzati Aitor Arnaiz

Abstract

Maintenance is a research field that has recently been gaining importance in business and where the study and development of monitoring and predictive technologies has been very active, as the role of these technologies is key in enabling predict and prevent maintenance strategies. Moreover, by means of monitoring features of processes and components, an impact in lifecycle value can be achieved. However, challenges remain in structuring the condition monitoring offer and the technological platform due, in particular, to the variety of potential domains of application, the characteristics of the existing information and the final goals of the monitoring activities. These challenges may impact in the deployment time of a condition monitoring solution. In order to limit these challenges, a methodology for fast deployment of condition monitoring and a technological service platform is presented. The methodology has been obtained from research and analysis of several use cases in the context of product-service systems. The focus is on methodological and technological results, which are presented in a general manner such that they can be applicable to the deployment of condition monitoring and services in various domains. Finally, application of the methodology is presented in two different scenarios.

How to Cite

Fernandez, S., Mozzati, C., & Arnaiz, A. (2016). A Methodology for Fast Deployment of Condition Monitoring and Generic Services Platform Technological Design. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1650
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Keywords

PHM

References
Alrabghi, A., & Tiwari, A. (2013). A Review of Simulation-based Optimisation in Maintenance Operations. 15th International Conference on Computer Modelling and Simulation (UKSim), pp. 353–358.
Alves, M. R. J., de Oliveira Bizarria, C., & Galvão, R. K. H. (2009). Trend analysis for prognostics and health monitoring. Proceedings of the 2009 Brazilian Symposium on Aerospace Engineering and Applications / 3rd CTA-DLR Workshop on Data Analysis and Flight Control. S. J. Campos, SP, Brazil.
Bengtsson, M., (2007). On Condition Based Maintenance and its Implementation in Industrial Settings. Doctoral dissertation. Mälardalen University, Sweden.
Chen, Z. S., Yang, Y. M., & Hu, Z. (2012). A technical framework and roadmap of embedded diagnostics and prognostics for complex mechanical systems in prognostics and health management systems. IEEE Transactions on Reliability, 61(2), pp. 314-322.
Discenzo, F. M., Nickerson, W., Mitchell, C. E., & Keller, K. J. (2001). Open systems architecture enables health management for next generation system monitoring and maintenance. Development Program White Paper, The OSA-CBM Development Group.
Gilabert, E., Fernandez, S., Arnaiz, A., & Konde, E. (2015). Simulation of predictive maintenance strategies for cost-effectiveness analysis. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.
Kmenta, S., & Ishii, K. (2000). Scenario-based FMEA: A life cycle cost perspective. Proceedings of DETC 2000 / 2000 ASME Design Engineering Technical Conferences. Baltimore, Maryland, U.S.A.
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). Intelligent prognostics tools and e-maintenance. Computers in Industry, 57, pp. 476-489.
Line, J. K., & Clements, N. S. (2005). A systematic approach for developing prognostic algorithms on large complex systems. Proceedings of IEEE Aerospace Conference, pp. 1-7.
Márquez A. C., de León P. M., Fernández J. F. G., Márquez C. P. & González V. (2009). The maintenance management framework: A practical view to maintenance management. Safety, Reliability and Risk Analysis: Theory, Methods and Applications, Martorell et al. (eds), Taylor & Francis Group, pp. 669-674.
Muller, A., Suhner, M-C., & Benoît, I. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering and System Safety, 93(2), pp.234-253.
Pandian, A., & Ali, A. (2010). A review of recent trends in machine diagnosis and prognosis algorithms. International Journal of Computer Information Systems and Industrial Management Applications, 2, pp.320-328.
Puttini, L. C., & Fitzgibbon, K. T. (2008). An overview of testing methodologies for the development of PHM solutions. Proceedings of the 2008 International Conference on Prognostics and Health Management.
Rhee, S. J., & Ishii, K. (2003). Using cost based FMEA to enhance reliability and serviceability. Advanced Engineering Informatics, 17, pp. 179-188.
Roemer, M. J., Byington, C. S., Kacprzynski, G. J., & Vachtsevanos, G. (2005). An overview of selected prognostic technologies with reference to an integrated PHM architecture. Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace.
Section
Technical Papers