The application of big data-driven prognostic and health management on complex equipment based on internet of things

DOI

10.2991/eeeis-16.2017.106How to use a DOI?

Keywords

Big data-driven prognostic; Prognostic and Health Management(PHM); complex equipment based on Internet of Things(IoT); big data; equipment health management)

Abstract

A novel big data-driven Prognostic and Health Management (PHM) method of Unmanned Air Vehicles (UAV) based on IoT was proposed. The method innovatively using big data of IoT analysis with health states to build the reference indicator, through probability-based techniques, and produced a degradation model to health monitoring UAV systems. This method was concerned with trend analysis and regression techniques for estimation of the future condition of IoT systems and prediction of the time-to-failure. The simulation results showed that the proposed method could give the health index of UAV systems visually and was proved to be practical and easy to implement in engineering.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guo-Shun Chen
AU  - Gang Niu
PY  - 2016/12
DA  - 2016/12
TI  - The application of big data-driven prognostic and health management on complex equipment based on internet of things
BT  - Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)
PB  - Atlantis Press
SP  - 862
EP  - 869
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-16.2017.106
DO  - 10.2991/eeeis-16.2017.106
ID  - Chen2016/12
ER  -