Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning
- University of Pittsburgh, PA (United States); OSTI
- University of Pittsburgh, PA (United States)
Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.
- Research Organization:
- University of Pittsburgh, PA (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- NE0008909
- OSTI ID:
- 1976817
- Journal Information:
- Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Journal Issue: C Vol. 168; ISSN 0306-4549
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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