Equipment Maintenance Support Decision Method Research Based on Big Data

  • Ziqiang Wang
  • Yuanzhou Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


Facing huge amounts of data in the field of equipment maintenance support, this paper studies the technique of equipment maintenance support data analysis and decision, formed information analysis technology and relevant methods for stages of Data acquisition, data integrate, theme data, online analysis and data mining, which is theory and practice basis of equipment maintenance support data analysis and decision support method research; It puts forward the trinity system framework for the technology of data storage, analysis and show. Through constructing safe and reliable storage environment, designing intelligent and efficient analysis algorithm, provide intuitive display form, it provides technical support for scientific decision based on the data.


Equipment support Big data Decision support 


  1. 1.
    Wang, F., Liu, J.C.: Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun. Surv. Tutor. 13, 673–687 (2011)CrossRefGoogle Scholar
  2. 2.
    Shi, J.H., Wan, J.F., Yan, H.H., et al.: A survey of cyber-physical systems. In: Proceedings of International Conference on Wireless Communications and Signal Processing, Nanjing, pp. 1–6 (2011)Google Scholar
  3. 3.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Newton (2012)Google Scholar
  4. 4.
    Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, New York (2011)Google Scholar
  5. 5.
    Meijer, E.: The world according to LINQ. Commun. ACM 54, 45–51 (2011)CrossRefGoogle Scholar
  6. 6.
    Borkar, V.R., Carey, M.J., Li, C.: Big data platforms: what’s next? XRDS: crossroads. ACM Mag. Students 19, 44–49 (2012)Google Scholar
  7. 7.
    Bryant, R.E.: Data-intensive scalable computing for scientific applications. Comput. Sci. Eng. 13, 25–33 (2011)CrossRefGoogle Scholar
  8. 8.
    Wang, X.Q.: Semantically-aware data discovery and placement in collaborative computing environments. Dissertation for Ph.D. Degree. Taiyuan University of Technology, Taiyuan (2012)Google Scholar
  9. 9.
    Middleton, S.E., Sabeur, Z.A., Löwe, P., et al.: Multi-disciplinary approaches to intelligently sharing large-volumes of real-time sensor data during natural disasters. Data Sci. J. 12, WDS109–WDS113 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Armored Force Engineering InstituteBeijingChina

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