A Software Architecture for Autonomous Maintenance Scheduling: Scenarios for UK and European Rail

A Software Architecture for Autonomous Maintenance Scheduling: Scenarios for UK and European Rail

Chris Turner | Prithyukshaa Thoppur Ravi Ashutosh Tiwari | Andrew Starr | Kevin Blacktop

Manufacturing and Materials Department, Cranfield University, UK

Network Rail, The Quadrant, UK

Page: 
371-381
|
DOI: 
https://doi.org/10.2495/TDI-V1-N3-371-381
Received: 
N/A
|
Revised: 
N/A
|
Accepted: 
N/A
|
Available online: 
30 April 2017
| Citation

OPEN ACCESS

Abstract: 

A new era of automation in rail has begun offering developments in the operation and maintenance of industry standard systems. This article documents the development of an architecture and range of scenarios for an autonomous system for rail maintenance planning and scheduling. The Unified Modelling Language (UML) has been utilized to visualize and validate the design of the prototype. A model for information exchange between prototype components and related maintenance planning systems is proposed in this article. Putting forward an architecture and set of usage mode scenarios for the proposed system, this article outlines and validates a viable platform for autonomous planning and scheduling in rail.

Keywords: 

decision support systems, rail planning and scheduling, software architecture

  References

[1] GB rail industry financial information 2013–14 (Office of Rail Regulation), availableat http://orr.gov.uk/__data/assets/pdf_file/0005/16997/gb-rail-industry-financials-2013-14.pdf (accessed 4 February 2016).

[2] Cores, F., Caceres, N., Benitez, F. G., Escriba, S. & Jimenez Redondo, N., A logicalframework and integrated architecture for the rail maintenance automation. In EuropeanTransport Conference 2013, 2013/9/30 to 2013/10/2, Frankfurt.

[3] AUTONOM (integrated through life support for high-value systems), available at https://www.cranfield.ac.uk/research/research-activity/current-projects/research-projects/autonom.html (accessed 4 February 2016).

[4] Turner, C., Tiwari, A., Starr, A. & Blacktop, K., A review of key planning and schedulingin the rail industry in Europe and UK. Proceedings of the Institution of MechanicalEngineers, Part F: Journal of Rail and Rapid Transit, 230(3), 2015. DOI:10.1177/0954409714565654.

[5] Mary, S.R. & Rodrigues, P., Software architecture-evolution and evaluation. InternationalJournal of Advanced Computer Science and Applications 3(8), pp. 82–88, 2012.DOI: 10.14569/IJACSA.2012.030814.

[6] Berkenkötter, K., Bisanz, S., Hannemann, U. & Peleska, J., Executable hybrid UMLand its application to train control systems. Integration of Software Specification Techniquesfor Applications in Engineering, eds. H. Ehrig, W. Damm, M. Große-Rhode,W. Reif, E. Schnieder & Westkamper, Springer Verlag: Berlin, Germany, pp. 145–173,2004.

[7] Berkenkötter, K. & Hannemann, U., Modeling the railway control domain rigorouslywith a uml 2.0 profile. Computer Safety, Reliability, and Security (LNCS – 4166), ed. J.Górski, Springer, Heidelberg: Springer, pp. 398–411, 2006.

[8] Caprara, A., Kroon, L., Monaci, M., Peeters, M. & Toth, P., Passenger railway optimization.Handbooks in Operations Research and Management Science 14, pp. 129–187,2007. DOI: 10.1016/S0927-0507(06)14003-7.

[9] Klabes, S.G., Algorithmic railway capacity allocation in a competitive European railwaymarket, Doctoral dissertation, PhD thesis, RWTH Aachen, 2010.

[10] Camci, F. & Chinnam, R.B., Process Monitoring, Diagnostics and Prognostics in MachiningProcesses, Saarbrücken, Germany: LAP Lambert Academic Publishing, 2010.

[11] Dadashi, N., Wilson, J.R., Sharples, S., Golightly, D. & Clarke, T., A framework of dataprocessing for decision making in railway intelligent infrastructure. 2011 IEEE FirstInternational Multi-Disciplinary Conference on Cognitive Methods in Situation Awarenessand Decision Support (CogSIMA), Miami Beach, FL, IEEE, pp. 276–283, 2011.

[12] Schlake, B.W., Barkan, C.P.L. & Riley Edwards, J., Train delay and economic impactof in-service failures of railroad rolling stock. Journal of the Transportation ResearchBoard, 2261, pp. 124–133, 2011. DOI: 10.3141/2261-14.

[13] Cresswell, S.N., McCluskey, T.L. & West, M.M., Acquiring planning domain modelsusing LOCM. Knowledge Engineering Review, 28, pp. 195–213, 2013. DOI: 10.1017/S0269888912000422.

[14] Fernández, S., De La Rosa, T., Fernández, F., Suárez, R., Ortiz, J., Borrajo, D. & Manzano,D., Using automated planning for improving data mining processes. KnowledgeEngineering Review, 28, pp. 157–173, 2013. DOI: 10.1017/S0269888912000409.

[15] McDermott, D., The AIPS’98 Planning Competition Committee. PDDL–the planningdomain definition language. Tech. rep., available at http://www.cs.yale.edu/homes/dvm/ (accessed 18 September 2015).

[16] Verstichel, S., Ongenae, F., Loeve, L., Vermeulen, F., Dings, P., Dhoedt, B. & De Turck,F., Efficient data integration in the railway domain through an ontology-based methodology.Transportation Research Part C: Emerging Technologies, 19(4), pp. 617–643,2011. DOI: 10.1016/j.trc.2010.10.003.

[17] Marcano, R. & Levy, N., Using B formal specifications for analysis and verification ofUML/OCL models. In Workshop on consistency problems in UML-based software development.5th International Conference on the Unified Modeling Language, Dresden,Germany, pp. 91–105, 2002.