Sensors incipient fault detection and isolation of nuclear power plant using extended Kalman filter and Kullback-Leibler divergence
| Autor: | Prakash Kumar Tamboli, Siddhartha P. Duttagupta, Kallol Roy, V.H. Patankar, Suryakant Gautam |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Kullback–Leibler divergence Computer science Applied Mathematics 020208 electrical & electronic engineering 02 engineering and technology Fault (power engineering) Fault detection and isolation Computer Science Applications Extended Kalman filter 020901 industrial engineering & automation Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering A priori and a posteriori Control chart EWMA chart Electrical and Electronic Engineering Divergence (statistics) Instrumentation |
| Zdroj: | ISA transactions. 92 |
| ISSN: | 1879-2022 |
| Popis: | Sensor real-time monitoring is an indispensable to achieve reliable plant operation along with stricter safety and environmental measures. This paper presents a statistical algorithm for sensors time-varying incipient fault detection and isolation. The proposed approach formulates the fault detection index and fault signature using the extended Kalman filter. Algorithm relaxes assumption on a monitored system stability and a priori knowledge of the fault profile. Further, fault decision statistics has been devised using Kullback-Leibler Divergence (KLD) and mixed with an Exponential Weighted Moving Average (EWMA) control chart. Pressurized water reactor nuclear power plant temperature and neutron flux sensors incipient fault detection and isolation have been demonstrated to illustrate the effectiveness of proposed methodology. |
| Databáze: | OpenAIRE |
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