Abstract
• Multi-sensor information fusion methods are used to estimate sensor errors. • Multiple information fusion implementations have comparable performance for sensor error estimation. • Generalization equations provide confidence probabilities for sensor error estimates using multi-sensor fusion.
| Original language | English |
|---|---|
| Article number | 111024 |
| Journal | Nuclear Engineering and Design |
| Volume | 375 |
| DOIs | |
| State | Published - Apr 15 2021 |
Funding
The authors wish to acknowledge AMS Corp. for providing the data used in this study. This work is supported by DOE NE program under Sensor Calibration Project and by DOE ASCR Applied Mathematics Program under Cyber Physical Networks project. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
Keywords
- Machine learning
- Multiple sensor fusion
- Nuclear power plant
- Primary coolant system
- Senor errors
- Sensor Drift