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Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport

French institute of science and technology for transport, spatial planning, development and networks, Scientific Direction, 14/20 Boulevard Newton, 77447 Marne la Vallée, France

Topical Section: Artificial Intelligence and Machine Learning

As part of the process of certification and commissioning of a new guided or automated rail transport system, the domain experts and in particular the National Safety Authority are responsible for reviewing the safety of the system to ensure that the safety level of the new transport system is at least equivalent to the railway systems already in service and deemed safe. This critical task of evaluating safety essentially concerns all the safety files prepared by the manufacturer and in particular safety studies such as the Preliminary Hazard Analysis (PHA), the functional safety analysis (FSA), the analysis of failure modes, their effects and of their criticality (AFMEC) or Software Error Effect Analysis (SEEA). The study presented in this paper is part of the SEEA analysis. To respect the completeness and consistency of this safety analysis (SEEA), the experts carry out complementary analyses of safety. They are brought to imagine new scenarios of potential accidents to perfect the exhaustiveness of the safety studies. In this process, one of the difficulties then consists in finding the abnormal scenarios being able to lead to a particular potential accident. This is the fundamental point that motivated this work. To help experts in this complex process of evaluating safety studies, we agreed to use artificial intelligence techniques and in particular machine learning to systematize, streamline and strengthen conventional approaches to safety analysis and critical software certification. The approach which was adopted in order to design and implement an assistance tool for safety analysis involved the following two main activities:
– Extracting, formalizing and storing hazardous situations to produce a library of standard cases which covers the entire problem. This process entailed the use of knowledge acquisition techniques;
– Exploiting the stored historical knowledge in order to develop safety analysis know-how which can assist experts to judge the thoroughness of the manufacturer’s suggested safety analysis. This second activity involves the use of machine learning techniques in particular the use of case-based reasoning (CBR).
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References

1. CENELEC-EN 50129 (2003) Railway applications - Communication, signaling and processing systems - Safety related electronic systems for signaling, 1 February 2003, 98.

2. Aamodt Norme AFNOR (1990) Installations fixes et matériel roulant ferroviaires. Informatique - Sûreté de fonctionnement des logiciels, Norme française F 71012 et F 71 013, 1990.

3. Thireau P (1986) Méthodologie d'Analyse des Effets des Erreurs du Logiciel (AEEL) appliquée à l'étude d'un logiciel de haute sécurité. 5° colloque international de fiabilité et de maintenabilité, Biarritz, France, 1986.

4. Hadj-Mabrouk H (2007) Contribution du raisonnement à partir de cas à l'analyse des effets des erreurs du logiciel. Application à la sécurité des transports ferroviaires, Ouvrage collectif, Raisonnement à partir de cas, Volume 2, chapitre 4, Éditions Hermes/Lavoisier, 123-148.

5. Mabrouk HH (2017) Machine learning from experience feedback on accidents in transport. 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 246-251.

6. Hadj-Mabrouk H (2017) Contribution of learning Charade system of rules for the prevention of rail accidents. Intell Decis Technol 11: 477-485.    

7. Hadj-Mabrouk H, (2018) A Hybrid Approach for the Prevention of Railway Accidents Based on Artificial Intelligence, In: Vasant P, Zelinka I, Weber GW (eds.), International Conference on Intelligent Computing & Optimization, 383-394.

8. Aussenac G, Gandon F (2013) From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition. Int J Hum-Comput St 71: 157-165.    

9. Gaines BR (2012) Knowledge acquisition: Past, present, and future. Int J Hum-Comput St 71: 135-156.

10. Dieng R (1990) Méthodes et outils d'acquisition des connaissances. ERGO IA90, Biarritz, France, 19 à 21 septembre.

11. Kodratoff Y (1986) Leçons d'apprentissage symbolique automatique. Cepadues éd., Toulouse, France.

12. Ganascia JG (2007) L'intelligence artificielle. Cavalier Bleu Eds. ISBN: 978-2-84670-165-5, 128.

13. Ganascia JG (2011) Logical Induction, Machine Learning and Human Creativity. Switching Codes, University of Chicago Press, ISBN: 978022603830, 2011.

14. Michalski RS, Wojtusiak J (2012) Reasoning with missing, not-applicable and irrelevant meta-values in concept learning and pattern discovery. J Intell Inf Syst 39: 141-166.    

15. Jamal S, Goyal S, Grover A, et al. (2018) Machine Learning: What, Why, and How? In: Shanker A (Eds.), Bioinformatics: Sequences, Structures, Phylogeny, Springer, Singapore, 359-374.

16. Aamodt A, Plaza E (1994) Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun 7: 39-52.

17. Harmon P (1991) Case-based reasoning II. Intelligent Software Strategies, 7: 1-9.

18. Kolodner J (1992) An introduction to case-based reasoning. Artif Intell Rev 6: 3-34.    

19. Kolodner J (1993) Case-Based Reasoning. Morgan-Kaufmann Pub. Inc., 668.

20. Leake D, (1996) CBR in Context: The present and future, In: Leake D (ed.), Case-Based Reasoning: Experiences, Lessons, and Future Directions, AAAI Press/MIT Press, 1-30.

21. Mott S (1993) Case-based reasoning: Market, applications, and fit with other technologies. Expert Syst Appl 6: 97-104.    

22. Pinson S, Demourioux M, Laasri B, et al. (1993) Le Raisonnement à Partir de Cas: panorama et modélisation dynamique. Séminaire CBR, LAFORIA, Rapport 93/42, 1er octobre 1993.

23. Slade S (1991) Case-based reasoning: A research paradigm. AI Mag 12: 42-55.

24. Bergmeir C, Sáinz G, Bertrand CM, et al. (2013) A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks, In: Ali M, Bosse T, Hindriks KV, Hoogendoorn M, Jonker CM, Treur J (eds.), Recent Trends in Applied Artificial Intelligence, IEA/AIE 2013, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 7906: 674-683.

25. Fay A (2000) A fuzzy knowledge-based system for railway traffic control. Eng Appl Artif Intel 13: 719-729.    

26. Santur Y, Karaköse M, Akin E (2017) A new rail inspection method based on deep learning using laser cameras. International Artificial Intelligence and Data Processing Symposium (IDAP), 16-17 Sept. 2017.

27. Faghih-Roohi S, Hajizadeh S, Núñez A, et al. (2016) Deep convolutional neural networks for detection of rail surface defects. International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016, Canada.

28. Ghofrania F, He Q, Goverde R, et al. (2018) Recent applications of big data analytics in railway transportation systems: A survey. Transport Res C-Emer 90: 226-246.    

29. Thaduri A, Galar D, Kumar U (2015) Railway assets: A potential domain for big data analytics. Procedia Comput Sci 53: 457-467.    

30. Attoh-Okine N (2014) Big data challenges in railway engineering. IEEE International Conference on Big Data (Big Data), 27-30 Oct. 2014, Washington, DC, USA.

31. Peter Hughes (2018) Making the railway safer with big data. Available from: http://www.railtechnologymagazine.com/Comment/making-the-railway-safer-with-big-data.

32. Vicki Hayward (2018) Big data & the Digital Railway. Available from: https://on-trac.co.uk/big-data-digital-railway/.

33. Bernard Marr (2017) How Siemens Is Using Big Data And IoT To Build The Internet Of Trains. Available from: https://www.forbes.com/sites/bernardmarr/2017/05/30/how-siemens-is-using-big-data-and-iot-to-build-the-internet-of-trains/#2b7a4b6e72b8.

34. Zubair M, Khan MJ, Awais M (2012) Prediction and analysis of air incidents and accidents using case-based reasoning. Third Global Congress on Intelligent Systems, 6-8 Nov. 2012, Wuhan, China.

35. Khattak A, Kanafani A (1996) Case-based reasoning: A planning tool for intelligent transportation systems. Transport Res C-Emer 4: 267-288.    

36. Sadeka A, Smith B, Demetsky M (2001) A prototype case-based reasoning system for real-time freeway traffic routing. Transport Res C-Emer 9: 353-380.    

37. Sadek A, Demetsky M, Smith B (2002) Case-Based Reasoning for Real-Time Traffic Flow Management. Comput-Aided Civ Inf.

38. Zhenlong L, Xiaohua Z (2008) A case-based reasoning approach to urban intersection control. 7th World Congress on Intelligent Control and Automation, 25-27 June 2008, Chongqing, China.

39. Li K, Waters NM, (2005) Transportation Networks, Case-Based Reasoning and Traffic Collision Analysis: A Methodology for the 21st Century, In: Reggiani A, Schintler LA (eds.), Methods and Models in Transport and Telecommunications, Advances in Spatial Science. Springer, Berlin, Heidelberg, 63-92.

40. Kofod-Petersen A, Andersen OJ, Aamodt A, (2014) Case-Based Reasoning for Improving Traffic Flow in Urban Intersections, In: Lamontagne L, Plaza E (eds.), Case-Based Reasoning Research and Development, ICCBR 2014, Lecture Notes in Computer Science, Springer, Cham, 8765: 215-229.

41. Louati A, Elkosantini S, Darmoul S, et al. (2016) A case-based reasoning system to control traffic at signalized intersections. IFAC-Papers On Line 49: 149-154.

42. Begum S, Ahmed MU, Funk P, et al. (2012) Mental state monitoring system for the professional drivers based on Heart Rate Variability analysis and Case-Based Reasoning. Federated Conference on Computer Science and Information Systems (FedCSIS), 9-12 Sept. 2012, Wroclaw, Poland.

43. Zhong Q, Zhang G, (2017) A Case-Based Approach for Modelling the Risk of Driver Fatigue, In: Shi Z, Goertzel B, Feng J (eds.), Intelligence Science I. ICIS 2017. IFIP Advances in Information and Communication Technology, Springer, Cham, 510: 45-56.

44. Varma A, Roddy N (1999) ICARUS: Design and deployment of a case-based reasoning system for locomotive diagnostics. Eng Appl Artif Intel 12: 681-690.    

45. Johnson C (2000) Using case-based reasoning to support the indexing and retrieval of incident reports. Proceeding of European Safety and Reliability Conference (ESREL 2000): Foresight and Precaution, Balkema, Rotterdam, the Netherlands, 1387-1394.

46. Cui Y, Tang Z, Dai H (2005) Case-based reasoning and rule-based reasoning for railway incidents prevention. Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 13-15 June 2005, Chongquing, China.

47. Li X, Yu K (2010) The research of intelligent Decision Support system based on Case-based Reasoning in the Railway Rescue Command System. International Conference on Intelligent Control and Information Processing, 13-15 Aug. 2010, Dalian, China.

48. Lu Y, Li Q, Xiao W (2013) Case-based reasoning for automated safety risk analysis on subway operation: Case representation and retrieval. Safety Sci 57: 75-81.    

49. de Souza VDM, Borges AP, Sato DMV, et al. (2016) Automatic knowledge learning using Case-Based Reasoning: A case study approach to automatic train conduction. International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016.

50. Zhao H, Chen H, Dong W, et al. (2017) Fault diagnosis of rail turnout system based on case-based reasoning with compound distance methods. 29th Chinese Control And Decision Conference (CCDC), 28-30 May 2017.

51. Darricau M (1995) Apport du raisonnement à partir de cas à l'analyse des effets des erreurs de logiciels. Application à la sécurité des logiciels critiques, Rapport de fin d'études d'ingénieur, INRETS-IFSTTAR, juin 1995.

52. Darricau M, Hadj-Mabrouk H (1996) Applying case-based reasoning to the storing and assessment of software error-effect analysis in railway Systems. Comprail 96, 5th International Conference on Computer-Aided Design, Construction and Operation in Railway Transport Systems, Berlin, 483-492.

53. Quinlan JR (1986) Induction of Decision Trees. Mach Learn 1: 81-106.

54. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27: 379-423.    

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