Extending RISMC Capabilities for Real-Time Diagnostics and Prognostics
Quick and effective accident management is essential in any industry in order to limit and contain possible threats to both people and environment/assets. This is in particular relevant in the nuclear industry where accidents may have major impacts from an economic, health and societal point of view. As an example, the Fukushima Dai-ichi nuclear power plant accident highlighted the importance of the ability of plant operators and plant staff to react quickly and effectively in accident conditions. This particular event showed the importance of being able to: • Determine/estimate the actual status of the plant (i.e., diagnosis) when the monitoring system is corrupted or partially unavailable, and, • Forecast its future evolution (i.e., prognosis). In this paper we want to describe a research direction geared toward the development of a new set of advanced diagnosis and prognosis tools. We employ innovative data mining and machine learning techniques that are able to infer plant status and mimic the plant’s full temporal behavior in order to assist the reactor operators during an accident scenario.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- DOE - NE
- DOE Contract Number:
- DE-AC07-05ID14517
- OSTI ID:
- 1173102
- Report Number(s):
- INL/CON-14-32495
- Resource Relation:
- Conference: ANS winter meeting,anheim (CA),11/09/2014,11/13/2014
- Country of Publication:
- United States
- Language:
- English
Similar Records
Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite
On-line fission products measurements during a PWR severe accident: the French DECA-PF project