Introduction and Need for Maintenance in Transportation: A Way Towards Smart Maintenance

  • Miguel Castaño ArranzEmail author
Part of the Asset Analytics book series (ASAN)


A smart technology can sense its environment and perform tasks through complex non-conventional reasoning. This leads to subjective and fashionable classifications of technologies as “smart” or “not smart”. Undoubtedly, technologies such as vehicle cruise controllers could have been considered as smart in the past but are now too conventional to be considered as such (Santacana and Rackliffe in Power and Energy, 41–48, 2010). A smart parking technology using RFID technology to check in and check out vehicles (Pala and Inanç in Smart parking applications using RFID technology. 2007 1st Annual RFID Eurasia, 2007) is now very conventional. Complex reasoning is subjective to evaluate, and a general requirement is that smart technology should be able to perform its task which is thought to require human intelligence.


Maintenance Artificial intelligence Transport systems Smart technologies Mobility 


  1. Alippi, C., et al. (2000). Composite real-time image processing for railways track profile measurement. IEEE Transactions on Instrumentation and Measurement, 49(3), 559–564.CrossRefGoogle Scholar
  2. Alom, M. Z., et al. (2018). The history began from AlexNet: A comprehensive survey on deep learning approaches. Computer Vision and Pattern Recognition.Google Scholar
  3. Arranz, M. C., & Carlson, J. E. (2012). 3D synthetic aperture imaging using a water-jet coupled large-aperture single transducer. In 2014 IEEE International Ultrasonics Symposium, 1, 3.
  4. Azuma, R., et al. (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47. Scholar
  5. Bartsch-Spörl, B., Lenz, M., & Hübner, A. (1999). Case-based reasoning—Survey and future directions. In German Conference on Knowledge-Based Systems (pp. 67–89).Google Scholar
  6. Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13–21.CrossRefGoogle Scholar
  7. Brown, A. K., & Lu, Y. (2004, September). Performance test results of an integrated GPS/MEMS inertial navigation package. In Proceedings of the 17th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2004 (pp. 825–832).Google Scholar
  8. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5. Scholar
  9. Cheriet, M., Said, J. N., & Suen, C. Y. (1998). A recursive thresholding technique for image segmentation. IEEE Transactions on Image Processing, 7(6), 918–921. Scholar
  10. Dalal, N., & Triggs, W. (2004). Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR05, 1(3), 886–893.
  11. Deng, J., et al. (2009). Imagenet: A large-scale hierarchical image database. In CVPR 2009. IEEE Conference on Computer Vision and Pattern Recognition, 2009 (pp. 248–255).Google Scholar
  12. Devaney, M., et al. (2005). Preventing failures by mining maintenance logs with case-based reasoning. In Meeting of the Society for Machinery Failure Prevention Technology.Google Scholar
  13. Devi, M. P. A., Latha, T., & Sulochana, C. H. (2015). Iterative thresholding based image segmentation using 2D improved Otsu algorithm. In Global Conference on Communication Technologies, GCCT 2015. IEEE, (Gcct) (pp. 145–149).
  14. Dissanayake, G., et al. (2001). The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE Transactions on Robotics and Automation, 17(5), 731.CrossRefGoogle Scholar
  15. El Faouzi, N. E., Leung, H., & Kurian, A. (2011). Data fusion in intelligent transportation systems: Progress and challenges—A survey. Information Fusion, 12(1), 4–10. Scholar
  16. El Najjar, M. E., & Bonnifait, P. (2003). A roadmap matching method for precise vehicle localization using belief theory and Kalman filtering. In Proceedings of ICAR03—The 11th International Conference on Advanced Robotics (pp. 1677–1682).Google Scholar
  17. Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611. Scholar
  18. Fiorentino, M., et al. (2014). Augmented reality on large screen for interactive maintenance instructions. Computers in Industry, 65(2), 270–278. Scholar
  19. Goh, C. Y., et al. (2012). Online map-matching based on hidden Markov model for real-time traffic sensing applications. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 776–781). IEEE.
  20. He, K., et al. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).Google Scholar
  21. Hein, G. W. (2000). From GPS and GLONASS via EGNOS to Galileo—Positioning and navigation in the third millennium. GPS Solutions, 3(4), 39–47. Scholar
  22. Henderson, S., & Feiner, S. (2011). Exploring the benefits of augmented reality documentation for maintenance and repair. IEEE Transactions on Visualization and Computer Graphics, 17(10), 1355–1368. Scholar
  23. Jiang, B., et al. (2015). Ultrasonic imaging through thin reverberating materials. Physics Procedia. Scholar
  24. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. In Control theory: Twenty-five seminal papers (1932–1981). IEEE.
  25. Kim, W., Jee, G.-I., & Lee, J. G. (2000). Efficient use of digital road map in various positioning for ITS. In IEEE 2000. Position Location and Navigation Symposium (Cat. No. 00CH37062) (pp. 170–176).
  26. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. Scholar
  27. Kretz, T., & Schreckenberg, M. (2007). Moore and more and symmetry. In N. Waldau, et al. (Eds.), Pedestrian and evacuation dynamics 2005 (pp. 297–308). Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
  28. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS).Google Scholar
  29. Levinson, J., et al. (2011). Towards fully autonomous driving: Systems and algorithms. In IEEE Intelligent Vehicles Symposium, Proceedings. IEEE (IV) (pp. 163–168).
  30. Lin, C. L., & Su, C. Y. (2016, May). Modified unsharp masking detection using Otsu thresholding and Gray code. In Proceedings of the IEEE International Conference on Industrial Technology (pp. 787–791). IEEE.
  31. Machin, M., et al. (2018). On the use of artificial intelligence techniques in intelligent transportation systems. In 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (pp. 332–337).
  32. Marino, F., et al. (2007). A real-time visual inspection system for railway maintenance: Automatic hexagonal-headed bolts detection. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 37(3), 418–428. Scholar
  33. Meyer-Hilberg, J., & Jacob, T. (1994). High accuracy navigation and landing system using GPS/IMU system integration. IEEE Aerospace and Electronic Systems Magazine, 9(7), 11–17. Scholar
  34. Mille, A. (2006). From case-based reasoning to traces-based reasoning. Annual Reviews in Control, 30(2), 223–232. Scholar
  35. Moore, E. F. (1962). Machine models of self-reproduction. In Proceedings of Symposia in Applied Mathematics (pp. 17–33).Google Scholar
  36. Olwal, A., Gustafsson, J., & Lindfors, C. (2008). Spatial augmented reality on industrial CNC-machines (Vol. 6804, p. 680409).
  37. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. Scholar
  38. Pala, Z., & Inanç, N. (2007). Smart parking applications using RFID technology. In 2007 1st Annual RFID Eurasia.
  39. Pan, R., Yang, Q., & Pan, S. J. (2007). Mining competent case bases for case-based reasoning. Artificial Intelligence, 171(16–17), 1039–1068. Scholar
  40. Papageorgiou, C. P., Oren, M., & Poggio, T. (n.d.). A general framework for object detection. In Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) (pp. 555–562). IEEE.
  41. Pyo, J. S., Shin, D.-H., & Sung, T.-K. (2001). Development of a map matching method using the multiple hypothesis technique. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585) (pp. 23–27).
  42. Quddus, M., & Washington, S. (2015). Shortest path and vehicle trajectory aided map-matching for low frequency GPS data. Transportation Research Part C: Emerging Technologies, 55, 328–339.CrossRefGoogle Scholar
  43. Quddus, M. A., Ochieng, W. Y., & Noland, R. B. (2006). Integrity of map-matching algorithms. Transportation Research Part C: Emerging Technologies, 14(4), 283–302. Scholar
  44. Rajashekar Reddy, P., Amarnadh, V., & Bhaskar, M. (2006). Evaluation of stopping criterion in contour tracing algorithms. International Journal of Computer Science and Information Technologies, 3(3), 3888–3894.Google Scholar
  45. Reiners, D., et al. (1998). Augmented reality for construction tasks: Doorlock assembly. Proceedings of IEEE and ACM IWAR, 98(1), 31–46.Google Scholar
  46. Russakovsky, O., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252. Scholar
  47. Saha, B., et al. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291–296. Scholar
  48. Santacana, E., & Rackliffe, G. (2010). Getting smart. Power and Energy, 41–48.Google Scholar
  49. Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics.Google Scholar
  50. Shift, and Crossing-Accidents. (1998). Obstruction detector using ultrasonic sensors for upgrading the safety of a level crossing (Vol. 543, pp. 20–23).Google Scholar
  51. Skog, I., & Handel, P. (2009). In-car positioning and navigation technologies. A survey. IEEE Transactions on Intelligent Transportation Systems, 10(1), 4–21. Scholar
  52. Smyth, B., & Keane, M. T. (1995). Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. International Joint Conference on Artificial Intelligence, 95, 377–383. Scholar
  53. Sundaram, M., et al. (2011). Histogram modified local contrast enhancement for mammogram images. Applied Soft Computing Journal, 11(8), 5809–5816. Scholar
  54. Syed, S., & Cannon, M. E. (2004). Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In Proceedings of the Institute of Navigation (ION) National Technical Meeting (pp. 1–12). USA. Scholar
  55. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In Computer Vision and Pattern Recognition.Google Scholar
  56. Wang, X., et al. (2008). Real-time radiographic non-destructive inspection for aircraft maintenance. In 17th World Conference on Nondestructive Testing (pp. 25–28), Shanghai, China, October 25–28, 2008.Google Scholar
  57. Wang, W., Duan, L., & Wang, Y. (2017). Fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm. Journal of Electrical and Computer Engineering.Google Scholar
  58. Waters, C. D. J. (1992). Expert systems for vehicle scheduling. In Artificial intelligence in operational research (pp. 215–225). Springer.Google Scholar
  59. Yang, Y., & Farrell, J. A. (2003). Magnetometer and differential carrier phase GPS-aided INS for advanced vehicle control. IEEE Transactions on Robotics and Automation, 19(2), 269–282. Scholar
  60. You, S., Krage, M., & Jalics, L. (2005). Overview of remote diagnosis and maintenance for automotive systems reprinted from: Vehicle diagnostics (SAE Technical Paper 2005-01-1428) (724).Google Scholar
  61. Yu, H. J., Wang, Z. G., Liu, X. Y., & Hu, D. A. (2015). Big data application in intelligent transport systems. Applied Mechanics and Materials, 734, 365–368.CrossRefGoogle Scholar
  62. Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8689, LNCS (PART 1), pp. 818–833). Scholar
  63. Zhao, X., et al. (2018). Image-based comprehensive maintenance and inspection method for bridges using deep learning. In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems (p. V002T05A017).Google Scholar
  64. Zhou, F., Dun, H. B. L., & Billinghurst, M. (2008, September). Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. In Proceedings—7th IEEE International Symposium on Mixed and Augmented Reality 2008, ISMAR 2008 (pp. 193–202).

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.eMaintenance Group, Division of Operation and Maintenance EngineeringLuleå University of TechnologyLuleåSweden

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