Journal article
Application of the multi-scale enveloping spectrogram to detect weak faults in a wind turbine gearbox
IET renewable power generation, Vol.11(5), pp.578-584
04/12/2017
DOI: 10.1049/iet-rpg.2016.0722
Abstract
The gearbox of a wind turbine involves multiple rotating components, each having a potential to be affected by a fault. Detecting weak faults of these components with traditional demodulation analysis is challenging. Multi-scale enveloping spectrogram (MuSEnS) decomposes a vibration signal into different frequency bands while simultaneously generating the corresponding envelope spectra. In this study, a MuSEnS-based diagnosis approach is applied to detect faults affecting the intermediate stage of a gearbox installed in an operating wind turbine. The MuSEnSs of 12 vibration channels have allowed to identify multiple fault features, including the weak fault of the big gear on the sun shaft. The effectiveness of the proposed fault diagnosis approach has been tested with industrial data and the faults themself have been confirmed with the disassembled gears.
Details
- Title: Subtitle
- Application of the multi-scale enveloping spectrogram to detect weak faults in a wind turbine gearbox
- Creators
- Zhiyong Ma - North China Electric Power UniversityWei Teng - North China Electric Power UniversityYibing Liu - North China Electric Power UniversityDameng Wang - North China Electric Power UniversityAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IET renewable power generation, Vol.11(5), pp.578-584
- Publisher
- The Institution of Engineering and Technology
- DOI
- 10.1049/iet-rpg.2016.0722
- ISSN
- 1752-1416
- eISSN
- 1752-1424
- Grant note
- 2015ZD15 / the Fundamental Research Funds for the Central Universities of China 15214307D / Science and Technology Plan Projects of Hebei 51305135 / National Natural Science Foundation of China 2015AA043702 / the National High Technology Research and Development Program of China (863 Program)
- Language
- English
- Date published
- 04/12/2017
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984186978602771
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