The purpose of this paper is to demonstrate how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved condition monitoring, better maintenance and increased uptime. This eMaintenance solution is based on the on-line data acquisition, integration and analysis leading to effective maintenance decision making.
In the proposed methodology, data are acquired from railway measurement stations to the eMaintenance cloud, where they are filtered, fused, integrated and analysed to assist maintenance decisions. Extensive consultation with stakeholders has resulted in the analysis of railway data.
The paper provides a concept for a web-based eMaintenance solution for railway maintenance stakeholders for making fact-based decisions and develops more efficient and economically sound maintenance policies. Train wheels reaching their maintenance and safety limits are visualised in grids and graphs to assist stakeholders in making the appropriate maintenance decisions.
In this paper the authors have demonstrated that the wheel profile and force data can be remotely collected through cloud utilisation. The information generated can be used for maintenance decision making. Similarly, other measurable data can also be utilised for maintenance decision making.
This paper describes the importance of eMaintenance solution through online data analysis to make effective and efficient railway maintenance decisions, as a case study.
The authors would like to thank Trafikverket (Swedish Transport Administration) for providing data for this study and JVTC and LKAB/MTAB for sponsoring research work. The authors would also like to express their sincere appreciation to eMaintenance Lab for achieving this work. Furthermore, authors would also like to thanks Associate Professor Aditya Parida for his valuable comments and feedback on drafts of this paper.
Kour, R., Tretten, P. and Karim, R. (2014), "eMaintenance solution through online data analysis for railway maintenance decision-making", Journal of Quality in Maintenance Engineering, Vol. 20 No. 3, pp. 262-275. https://doi.org/10.1108/JQME-05-2014-0026Download as .RIS
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