A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators

DOI

10.2991/iwama-16.2016.6How to use a DOI?

Keywords

predictive maintenance; hydro turbines generators; data-driven model; data mining

Abstract

Hydroelectricity as a renewable energy to respond the increasing population and environment crisis is widely used in the world. With the Hydro Turbines/Generators (HTG) being more and more complicated, the maintenance play a more and more important role in the production management in the hydro power plant. Many researches had concentrated on the predictive maintenance for the HTG in recent years. From the perspective of data-driven, this paper reviews and summarizes the key techniques regarding data acquisition, data processing, data analysis and data mining for the predictive maintenance of HTG. Especially, it place emphasis on the data-driven models for the diagnostics and prognostics. Finally, the paper concludes the current practices and presents a future research work.

Copyright

© 2016, 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  - Shewei Wang
AU  - Kesheng Wang
AU  - Zhe Li
PY  - 2016/11
DA  - 2016/11
TI  - A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators
BT  - Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SP  - 30
EP  - 35
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.6
DO  - 10.2991/iwama-16.2016.6
ID  - Wang2016/11
ER  -