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Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis

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Abstract

Autonomous Vehicles have the potential to change the urban transport scenario. However, to be able to safely navigate autonomously they need to deal with faults that its components are subject to. Therefore, Health Monitoring System is a essential component of the autonomous system, since allows Fault Detection and Diagnosis. In addition, Prognosis System is also important, since it allows predictive maintenance and safer decisions during vehicle navigation. This paper presents a Hierarchical Component-based Health Monitoring System with Fault Detection, Diagnosis and Prognosis using Dynamic Bayesian Network (DBN) with residue generation, a combination of knowledge-based and model-based detection, diagnosis and prognosis approaches. We evaluate the proposed Dynamic Bayesian Network using different machine learning metrics and a dataset with sensor readings gathered using the CaRINA II autonomous vehicle platform, and the CARLA simulator. Both simulated and experimental results demonstrated a positive performance of the DBNs even with high rate of missing data for some of the model’s variables.

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References

  1. Gomes, I.P., Wolf, D.F.: A health monitoring system with hybrid bayesian network for autonomous vehicle. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp. 260–265. IEEE (2019)

  2. Gomes, I.P., Bruno, D.R., Osório, F.S., Wolf, D.F.: Diagnostic analysis for an autonomous truck using multiple attribute decision making. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). IEEE (2018)

  3. Anderson, J.M., Nidhi, K., Stanley, K.D., Sorensen, P., Samaras, C., Oluwatola, O.A.: Autonomous vehicle technology: A guide for policymakers. Rand Corporation (2014)

  4. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. A Policy Pract. 77, 167–181 (2015)

    Google Scholar 

  5. Liu, S., Li, L., Tang, J., Wu, S., Gaudiot, J-L: Creating autonomous vehicle systems, vol. 6. Morgan & Claypool Publishers (2017)

  6. SAE, S.A.E.: J3016 standard: taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE (2014)

  7. Bagloee, S.A., Tavana, M., Asadi, M., Oliver, T.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Modern Transp. 24(4), 284–303 (2016)

    Google Scholar 

  8. Esperon-Miguez, M., John, P., Jennions, I.K.: A review of integrated vehicle health management tools for legacy platforms: challenges and opportunities. Progress Aerosp. Sci. 56, 19–34 (2013)

    Google Scholar 

  9. Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis: Part i: Quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)

    Google Scholar 

  10. Lanigan, P.E., Kavulya, S., Narasimhan, P., Fuhrman, T.E., Salman, M.A.: Diagnosis in automotive systems: A survey. Last accessed Sept 10, 2011 (2011)

  11. Koren, M., Alsaif, S., Lee, R., Kochenderfer, M.J.: Adaptive stress testing for autonomous vehicles. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1–7. IEEE (2018)

  12. Lee, R., Mengshoel, O.J., Saksena, A., Gardner, R., Genin, D., Silbermann, J., Owen, M., Kochenderfer, M.J.: Adaptive stress testing: Finding failure events with reinforcement learning. arXiv:1811.02188 (2018)

  13. Huang, W., Wen, D., Geng, J., Zheng, N-N: Task-specific performance evaluation of ugvs: Case studies at the ivfc. IEEE Trans. Intell. Transp. Syst. 15(5), 1969–1979 (2014)

    Google Scholar 

  14. Crestani, D., Godary-Dejean, K., Lapierre, L.: Enhancing fault tolerance of autonomous mobile robots. Robot. Auton. Syst. 68, 140–155 (2015)

    Google Scholar 

  15. Ferreiro, S., Arnaiz, A., Sierra, B., Irigoien, I.: Application of bayesian networks in prognostics for a new integrated vehicle health management concept. Expert Syst. Appl. 39(7), 6402–6418 (2012)

    Google Scholar 

  16. Zong, W., Zhang, C., Wang, Z., Zhu, J., Chen, Q.: Architecture design and implementation of an autonomous vehicle. IEEE Access 6, 21956–21970 (2018)

    Google Scholar 

  17. Jo, K., Kim, J., Kim, D., Jang, C., Sunwoo, M.: Development of autonomous car - part ii: A case study on the implementation of an autonomous driving system based on distributed architecture. IEEE Trans. Ind. Electron. 62(8), 5119–5132 (2015)

    Google Scholar 

  18. Wei, J., Snider, J.M., Kim, J., Dolan, J.M., Rajkumar, R., Litkouhi, B.: Towards a viable autonomous driving research platform. In: 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE (2013)

  19. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques part ii: Fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 62(6), 3768–3774 (2015jun)

    Google Scholar 

  20. Cai, B., Huang, L., Xie, M.: Bayesian networks in fault diagnosis. IEEE Trans. Indust. Inf. 13(5), 2227–2240 (2017)

    Google Scholar 

  21. Amin, M.T., Imtiaz, S., Khan, F.: Process system fault detection and diagnosis using a hybrid technique. Chem. Eng. Sci. 189, 191–211 (2018)

    Google Scholar 

  22. Xu, J., Xu, L.: Chapter two - sensor system and health monitoring. In: Xu, J, Xu, L (eds.) Integrated System Health Management, pp 55–99. Academic Press (2017)

  23. Wang, D., Yu, M., Low, C.B., Arogeti, S.: Model-based health monitoring of hybrid systems. Springer (2013)

  24. Barua, A., Khorasani, K.: Hierarchical fault diagnosis and health monitoring in satellites formation flight. IEEE Trans. Syst. Man. Cybern. Part C (Appl. Rev.) 41(2), 223–239 (2010)

    Google Scholar 

  25. Schein, J., Bushby, S.T.: A hierarchical rule-based fault detection and diagnostic method for hvac systems. Hvac&r Res. 12(1), 111–125 (2006)

    Google Scholar 

  26. Rizzoni, G., Onori, S., Rubagotti, M.: Diagnosis and prognosis of automotive systems: motivations, history and some results. IFAC Proc. Vol. 42(8), 191–202 (2009)

    Google Scholar 

  27. Nasri, O., Lakhal, N.M.B., Adouane, L., Slama, J.B.H.: Automotive decentralized diagnosis based on can real-time analysis. J. Syst. Archit. 98, 249–258 (2019)

    Google Scholar 

  28. Stanley, G., et al.: A guide to fault detection and diagnosis (2013)

  29. Judalet, V., Glaser, S., Gruyer, D., Mammar, S.: Fault detection and isolation via the interacting multiple model approach applied to drive-by-wire vehicles. Sensors 18(7) (2018)

  30. Loureiro, R., Benmoussa, S., Touati, Y., Merzouki, R., Bouamama, B.O.: Integration of fault diagnosis and fault-tolerant control for health monitoring of a class of mimo intelligent autonomous vehicles. IEEE Trans. Veh. Technol. 63(1), 30–39 (2014)

    Google Scholar 

  31. Yu, M., Wang, D.: Model-based health monitoring for a vehicle steering system with multiple faults of unknown types. IEEE Trans. Indust. Electron. 61(7), 3574–3586 (2013)

    Google Scholar 

  32. Bader, K., Lussier, B., Schön, W: A fault tolerant architecture for data fusion: A real application of kalman filters for mobile robot localization. Robot. Auton. Syst. 88, 11–23 (2017)

    Google Scholar 

  33. Dai, X., Gao, Z.: From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Trans. Indust. Inf. 9(4), 2226–2238 (2013)

    Google Scholar 

  34. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniquespart i: Fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015)

    Google Scholar 

  35. Miljković, D.: Fault detection methods: A literature survey. In: 2011 Proceedings of the 34th international convention MIPRO, pp. 750–755. IEEE (2011)

  36. Tidriri, K., Tiplica, T., Chatti, N., Verron, S.: A generic framework for decision fusion in fault detection and diagnosis. Eng. Appl. Artif. Intel. 71, 73–86 (2018)

    Google Scholar 

  37. Byun, S., Yang, I., Song, M.G., Lee, D.: Reliability evaluation of steering system using dynamic fault tree. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 1416–1420. IEEE (2013)

  38. Hossain, A.: Diagnosis of autonomous vehicles using machine learning. Master’s Thesis, UPPSALA Universitet (2018)

  39. Pous, N., Gingras, D., Gruyer, D.: Fdi architecture for proprioceptive sensors using analytical redundancy and road signature. In: 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–6. IEEE (2015)

  40. Realpe, M., Vintimilla, B., Vlacic, L.: Sensor fault detection and diagnosis for autonomous vehicles. In: MATEC Web of Conferences, vol. 30, pp. 04003. EDP Sciences (2015)

  41. Simanek, J., Kubelka, V., Reinstein, M.: Improving multi-modal data fusion by anomaly detection. Auton. Robot. 39(2), 139–154 (2015)

    Google Scholar 

  42. Khalastchi, E., Kalech, M., Rokach, L.: Sensor fault detection and diagnosis for autonomous systems. In: Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, pp. 15–22. Citeseer (2013)

  43. Shafi, U., Safi, A., Shahid, A.R., Ziauddin, S., Saleem, M.Q.: Vehicle remote health monitoring and prognostic maintenance system. Journal of Advanced Transportation 2018 (2018)

  44. deMedeiros, I.P., Rodrigues, L.R., Santos, R., Shiguemori, E.H., Júnior, C.L.N.: Phm-based multi-uav task assignment. In: 2014 IEEE International Systems Conference Proceedings, pp. 42–49. IEEE (2014)

  45. Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N.: The iso 13381-1 standard’s failure prognostics process through an example. In: 2010 Prognostics and System Health Management Conference, pp. 1–12. IEEE (2010)

  46. Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N.: Cnc machine tool’s wear diagnostic and prognostic by using dynamic bayesian networks. Mech. Syst. Signal Process. 28, 167–182 (2012)

    Google Scholar 

  47. Peysson, F., Ouladsine, M., Outbib, R., Leger, J-B, Myx, O., Allemand, C.: Damage trajectory analysis based prognostic. In: 2008 International Conference on Prognostics and Health Management, pp. 1–8. IEEE (2008)

  48. Jammu, N.S., Kankar, P.K.: A review on prognosis of rolling element bearings. Int. J. Eng. Sci. Technol. 3(10), 7497–7503 (2011)

    Google Scholar 

  49. Ly, C., Tom, K., Byington, C.S., Patrick, R., Vachtsevanos, G.J.: Fault diagnosis and failure prognosis for engineering systems: A global perspective. In: 2009 IEEE International Conference on Automation Science and Engineering, pp. 108–115. IEEE (2009)

  50. Xu, J., Wang, Y., Xu, L.: Phm-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sens. J. 14(4), 1124–1132 (2013)

    Google Scholar 

  51. Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., Tripot, G.: Hidden markov models for failure diagnostic and prognostic. In: 2011 Prognostics and System Health Managment Confernece, pp. 1–8. IEEE (2011)

  52. Fox, C.: Bayesian inference, pp 75–92. Springer International Publishing, Cham (2018)

    Google Scholar 

  53. Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Pearson Education Limited, Malaysia (2016)

  54. Barber, D.: Bayesian reasoning and machine learning. Cambridge University Press (2012)

  55. Atoui, M.A., Verron, S., Kobi, A.: Fault detection with conditional gaussian network. Eng. Appl. Artif. Intel. 45, 473–481 (2015)

    Google Scholar 

  56. Cai, B., Liu, H., Xie, M.: A real-time fault diagnosis methodology of complex systems using object-oriented bayesian networks. Mech. Syst. Signal Process. 80, 31–44 (2016)

    Google Scholar 

  57. Huang, Y., McMurran, R., Dhadyalla, G., Jones, R.P.: Probability based vehicle fault diagnosis: Bayesian network method. J. Intell. Manuf. 19(3), 301–311 (2008)

    Google Scholar 

  58. Yu, H., Khan, F., Garaniya, V.: Modified independent component analysis and bayesian network-based two-stage fault diagnosis of process operations. Ind. Eng. Chem. Res. 54(10), 2724–2742 (2015)

    Google Scholar 

  59. Gharahbagheri, H., Imtiaz, S.A., Khan, F.: Root cause diagnosis of process fault using kpca and bayesian network. Ind. Eng. Chem. Res. 56(8), 2054–2070 (2017)

    Google Scholar 

  60. Duan, R-, Zhou, H-: A new fault diagnosis method based on fault tree and bayesian networks. Energy Procedia 17, 1376–1382 (2012)

    Google Scholar 

  61. Jun, H-B, Kim, D.: A bayesian network-based approach for fault analysis. Expert Syst. Appl. 81, 332–348 (2017)

    Google Scholar 

  62. Zhang, Y., You, L., Jia, C.: Fault detection and diagnosis using bayesian-network inference. In: IECON 2017-43rd Annual Conference of the IEEE, pp. 5049–5053. IEEE (2017)

  63. DAngelo, MarcosFSV, Palhares, R.M., Cosme, L.B., Aguiar, L.A., Fonseca, F.S., Caminhas, W.M.: Fault detection in dynamic systems by a fuzzy/bayesian network formulation. Appl. Soft Comput. 21, 647–653 (2014)

    Google Scholar 

  64. Xu, B.G.: Intelligent fault inference for rotating flexible rotors using bayesian belief network. Expert Syst. Appl. 39(1), 816–822 (2012)

    Google Scholar 

  65. Codetta-Raiteri, D., Portinale, L.: Dynamic bayesian networks for fault detection, identification, and recovery in autonomous spacecraft. IEEE Trans. Syst. Man. Cybern. Syst. 45(1), 13–24 (2014)

    Google Scholar 

  66. Cai, B., Liu, Y., Xie, M.: A dynamic-bayesian-network-based fault diagnosis methodology considering transient and intermittent faults. IEEE Trans. Autom. Sci. Eng. 14(1), 276–285 (2016)

    Google Scholar 

  67. Zhang, Z., Dong, F.: Fault detection and diagnosis for missing data systems with a three time-slice dynamic bayesian network approach. Chemom. Intell. Lab. Syst. 138, 30–40 (2014)

    Google Scholar 

  68. Schwall, M.L., Gerdes, J.C.: A probabilistic approach to residual processing for vehicle fault detection. In: Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), vol. 3, pp. 2552–2557. IEEE (2002)

  69. Iamsumang, C., Mosleh, A., Modarres, M.: Monitoring and learning algorithms for dynamic hybrid bayesian network in on-line system health management applications. Reliab. Eng. Syst. Saf. 178, 118–129 (2018)

    Google Scholar 

  70. Zhang, H., Zhang, Q., Liu, J., Guo, H.: Fault detection and repairing for intelligent connected vehicles based on dynamic bayesian network model. IEEE Internet Things J. 5(4), 2431–2440 (2018)

    Google Scholar 

  71. Rounsaville, J.D., Dvorak, J.S., Stombaugh, T.S.: Methods for calculating relative cross-track error for asabe/iso standard 12188-2 from discrete measurements. Trans. ASABE 59(6), 1609–1616 (2016)

    Google Scholar 

  72. Bijjahalli, S., Ramasamy, S., Sabatini, R.: A gnss integrity augmentation system for airport ground vehicle operations (2017)

  73. Dutt, V.S.I., Rao, G.S.B., Rani, S.S., Babu, S.R., Goswami, R., Kumari, C.U.: Investigation of gdop for precise user position computation with all satellites in view and optimum four satellite configurations. J. Ind. Geophys. Union 13(3), 139–148 (2009)

    Google Scholar 

  74. Kaplan, E., Hegarty, C.: Understanding gps: principles and applications. Artech house (2005)

  75. Jayaram, S.: A new fast converging kalman filter for sensor fault detection and isolation. Sensor Review (2010)

  76. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: An open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)

  77. Darwiche, A.: Modeling and reasoning with bayesian networks. Cambridge University Press (2009)

  78. Ng, S.K., Krishnan, T., McLachlan, G.J.: The em algorithm. Handbook of computational statistics, pp. 139–172. Springer (2012)

  79. Catal, C.: Performance evaluation metrics for software fault prediction studies. Acta Polytech. Hungarica 9(4), 193–206 (2012)

    Google Scholar 

  80. Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1 (2015)

    Google Scholar 

  81. Tharwat, A.: Classification assessment methods. Applied Computing and Informatics (2018)

  82. Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    MathSciNet  Google Scholar 

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Acknowledgements

We thank BayesFusion for the academic license of SMILE library, and CNPq, CAPES and FAPESP for the financial support.

Funding

This research was funded to the researcher Iago Pachêco Gomes, as a research grant, by the institutions:

- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES): Finance Code 001 and grant 88887.500344/2020-0

- Brazilian National Research Council (CNPq): grant 166874/2017-5

- São Paulo Research Foundation (FAPESP): grant 2019/27301-7

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  1. Institute of Mathematics and Computer Science Laboratory of Mobile Robotics, University of São Paulo, São Carlos, Brazil

    Iago Pachêco Gomes & Denis Fernando Wolf

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  1. Iago Pachêco Gomes
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  2. Denis Fernando Wolf
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Iago Pachêco Gomes. The first draft of the manuscript was written by Iago Pachêco Gomes, and revised by Denis Fernando Wolf. All authors read and approved the final manuscript.

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Correspondence to Iago Pachêco Gomes.

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This study was financed in part by the Coordenaç ão de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and grant 88887.500344/2020-00, the Brazilian National Research Council (CNPq) under grant 166874/2017-5, and the São Paulo Research Foundation (FAPESP) under grant 2019/27301-7. This paper was presented in part at PROCEEDINGS OF THE IEEE 2019 19th International Conference on Advanced Robotics (ICAR) [1] and IEEE 2018 Latin American Robotic Symposium (LARS) [2].

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Gomes, I.P., Wolf, D.F. Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis. J Intell Robot Syst 101, 19 (2021). https://doi.org/10.1007/s10846-020-01293-y

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