Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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

Authors
Shewei Wang, Kesheng Wang, Zhe Li
Corresponding Author
Shewei Wang
Available Online November 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.6How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.6
DO  - https://doi.org/10.2991/iwama-16.2016.6
ID  - Wang2016/11
ER  -