Dependability Analysis of High-Consequence Augmented Reality Systems

  • Ernest EdiforEmail author
  • Eleanor E. Cranmer
Part of the Progress in IS book series (PROIS)


Research on Augmented Reality (AR) has gained traction due to its plethora of benefits and range of applications. In high-consequence environments where the failure of a system can have devastating effects on human life and/or the environment, dependability (that is reliability and availability) are of utmost importance. Therefore, AR systems that form part of or constitute a high-consequence system need to be evaluated for their dependability. Unfortunately, AR research lacks a significant focus on this. Fault Tree Analysis (FTA) is a proven probabilistic risk analysis technique mainly used in engineering to analyse how the individual component failures of a system contribute to a total system failure. This research explores the use of an FTA-based technique for the dependability analysis of high-consequence AR systems. The proposed solution is applied to a real-world case study in the medical field and the results are discussed.


Augmented reality Fault tree analysis Risk analysis Monte Carlo simulation 


  1. Avizienis, A., & Laprie, J. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33.CrossRefGoogle Scholar
  2. Belhaoua, A., Kornmann, A., & Radoux, J. P. (2014). Accuracy analysis of an augmented reality system. In International Conference on Signal Processing Proceedings, ICSP (pp. 1169–1174). IEEE.Google Scholar
  3. Chen, M., Ling, C., & Zhang, W. (2011). Analysis of augmented reality application based on cloud computing. In Proceedings—4th International Congress on Image and Signal Processing, CISP 2011 (pp. 569–572). IEEE.Google Scholar
  4. Edifor, E. E. (2014). Quantitative analysis of dynamic safety-critical systems using temporal fault trees. The University of Hull. Retrieved from
  5. Edifor, E., Walker, M., & Gordon, N. (2012, September). Quantification of priority-OR gates in temporal fault trees. Computer Safety, Reliability, and Security.Google Scholar
  6. Edifor, E., Walker, M., & Gordon, N. (2013). Quantification of simultaneous-AND gates in temporal fault trees. Advances in Intelligent Systems and Computing, 224, 141–151.CrossRefGoogle Scholar
  7. Elia, V., Gnoni, M. G., & Lanzilotto, A. (2016). Evaluating the application of augmented reality devices in manufacturing from a process point of view: An AHP based model. Expert Systems with Applications, 63, 187–197.CrossRefGoogle Scholar
  8. Fussell, J. B., Aber, E. F., & Rahl, R. G. (1976). On the quantitative analysis of priority-and failure logic. IEEE Transactions on Reliability, 25(5), 324–326.CrossRefGoogle Scholar
  9. Goldsim. (2018). Goldsim. Retrieved December 15, 2018, from
  10. Graafland, E. Z. B. M., & Schijven, M. P. (2016). Systematic review on the effectiveness of augmented reality applications in medical training. Surgical Endoscopy, 30(10), 4174–4183.CrossRefGoogle Scholar
  11. Gulati, R., & Dugan, J. (1997). A modular approach for analyzing static and dynamic fault trees. In Reliability and Maintainability Symposium (pp. 57–63).Google Scholar
  12. Guo, H. L., Li, H., & Li, V. (2013). VP-based safety management in large-scale construction projects: A conceptual framework. Automation in Construction, 34(2013), 16–24.CrossRefGoogle Scholar
  13. Gürlük, H., Gluchshenko, O., Finke, M., Christoffels, L., & Tyburzy, L. (2018). Assessment of risks and benefits of context-adaptive augmented reality for aerodrome control towers. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1–10).Google Scholar
  14. King, C., Klinedinst, D., Lewellen, T., & Wassermann, G. (2016). 2016 emerging technology domains risk survey.Google Scholar
  15. Li, X., Yi, W., Chi, H., Wang, X., & Chan, A. P. C. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150–162.CrossRefGoogle Scholar
  16. Merle, G., Roussel, J., Lesage, J., & Bobbio, A. (2010). Probabilistic algebraic analysis of fault frees with priority dynamic gates and repeated events. IEEE Transactions on Reliability, 59(1), 250–261.CrossRefGoogle Scholar
  17. Pham, H. (2006). System reliability concepts. System software reliability (pp. 9–75). London: Springer.Google Scholar
  18. Schall, M. C., Rusch, M. L., Lee, J. D., Dawson, J. D. D., Thomas, G., Aksan, N., et al. (2013). Augmented reality cues and elderly driver hazard perception. Human Factors, 55(3), 643–658.CrossRefGoogle Scholar
  19. Vesely, W. E., Stamatelatos, M., Dugan, J. B., Fragola, J., Minarick, J., & Railsback, J. (2002). Fault tree handbook with aerospace applications. Washington, DC: NASA Office of Safety and Mission Assurance.Google Scholar
  20. Walker, M. D. (2009). Pandora: A logic for the qualitative analysis of temporal fault trees. The University of Hull.Google Scholar
  21. Walker, M., & Papadopoulos, Y. (2009). Qualitative temporal analysis: Towards a full implementation of the Fault Tree Handbook. Control Engineering Practice, 17(10), 1115–1125.CrossRefGoogle Scholar
  22. Zhu, M., Liu, F., Chai, G., Pan, J. J., Jiang, T., Lin, L., et al. (2017). A novel augmented reality system for displaying inferior alveolar nerve bundles in maxillofacial surgery. Scientific Reports, 7, 42365.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Business and LawManchester Metropolitan UniversityManchesterUK

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