Purpose: This paper presents a framework for simulation on IoT based CBM (condition based monitoring) for rolling stocks. This enables to allocate maintenance resources effectively while satisfying preventive maintenance requirements.
Methodology/Approach: We exploits Reliability centered maintenance (RCM) based on KTX (Korea Tran eXpress, Korea’s high-speed rail system) motor reduction unit failure data for three years by utilising the internet of things (IoT) and RAMS (Reliability, Availability, Maintainability, Safety) methods.
Findings: We come up with the predictive maintenance indicator; reliability functions as to the desired service level; and the failure and defect prediction indicator takes the form of cumulative failure function in the form of probability distribution function, which aim to realise the real-time condition monitoring and maintaining technical support services. Internet of Things (IoT) has been an important apparatus to improve the maintenance efficiency.
Research Limitation/implication: This paper has limitations that the data are collected from references, not actual data; the detailed descriptions of IoT application to the railway rolling stocks are omitted, and it is not dealt in depth how maintenance efforts and performance are improved through the suggested reliability centered maintenance.Originality/Value of paper: This study has the academic importance in a sense that it integrates RAMS based maintenance methods and IoT. RAMS centered maintenance provides powerful rules for deciding a failure management policy; when it is technically appropriate; and for providing precise criteria for deciding how often routine tasks should be carried out. It will lead to the improved cost efficiency, sustainability and maintainability of railway maintenance system since the staff do not have to visit installation sites frequently. Lately, there is general agreement that prevention was better than inspection and that an increase in preventive cost was the means of reducing total quality costs. In connection with this issue, we will address the way of reducing failure costs and prevention costs with IoT: new appraisal method.
Abril, M., Barber, F., Ingolotti, L., Salido, M.A., Tormos, P. and Lova, A., 2008. An Assessment of Railway Capacity. Transportation Research Part E: Logistics and Transportation Review, 44(5), pp.774-806.
Charnes, A. and Miller, M.H., 1956. A Model for the Optimal Programming of Railway Freight Train Movements. Management Science, 3(1), pp.74-92.
Duarte, J.C., Cunha, P.F. and Craveiro, J.T., 2013. Maintenance database. Procedia CIRP, 7, pp.551-556.
Hameed, Z., Vatn, J. and Heggset, J., 2011. Challenges in the reliability and maintainability data collection for offshore wind turbines. Renewable Energy, 36, pp.2154-2165.
Inman, D.J., Farrar, C.R. and Lopes, V., 2005. Damage prognosis: For aerospace, civil and mechanical systems. New York, NY: Wiley & Sons, Inc..
Jardine, A.K.S., Lin, D.M. and Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical Systems and Signal Processing, [e-journal] 20(7), pp.1483 - 1510. http://dx.doi.org/10.1016/j.ymssp.2005.09.012.
Kim, J.W., Park, J.-S., Lee, H.-Y. and Kim, J., 2008. A Conceptual Procedure of RAMS Centered Maintenance for Railway Systems. Journal of the Korean Society for Railway, 11(1), pp.19-25.
Levitin, G., 2005. Universal generating function in reliability analysis and optimization. London: Springer-Verlag.
Liao, H.T., Elsayed, E.A. and Chan, L.Y., 2006. Maintenance of continuously monitored degrading systems. European Journal of Operational Research, 175(2), pp.821-835.
Park, M.G., 2016. RAMS management of railway systems. Ph. D. University of Birmingham.
Price, C.J., Pragh, D.R., Wilson, M.S. and Snooke, N., 1995. The flame system: automating electrical failure mode and effects analysis (FMEA). Proceedings of Reliability Maintenance Symposium, pp.90-95.
Scarf, P., 1997. On the application of mathematical models in maintenance. European Journal of Operational Research, 99(3), pp.493-506.
Taylor, W.A., 1998. Methods and Tools for Process Validation. [online] Taylor Enterprises, Inc. Available at: < http://variation.com/techlib/val-1.html > [Accessed 20 July 2017].
Tian, Z. 2017. An Artificial Neural Network Approach For Remaining Useful Remaining Useful Life Prediction of Equipments Subject to Condition Monitoring. N.p., n.d. Web. 28 July 2017. Available at: < http://www.csaa.org.cn/uploads/xiazai/lwj/ICRMS'2009/section_01/01-33.pdf >.