ANN based sensor and actuator fault detection in nuclear reactors

Shohan Banerjee, Jiamei M. Deng, Chris Gorse, Vineet Vajpayee, Victor Becerra, S. R. Shimjith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data driven methods, and signal-based methods. In practical applications, model based methods are very difficult to achieve. Thus, various data-driven methods and signal-based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Control, Mechatronics and Automation, (ICCMA 2020)
Place of Publication978-1-7281-9211-6
PublisherInstitute of Electrical and Electronics Engineers
Pages88-94
Number of pages7
ISBN (Electronic)978-1-7281-9210-9, 978-1-7281-9209-3
DOIs
Publication statusPublished - 29 Dec 2020
EventThe 8th International Conference on Control, Mechatronics and Automation - Moscow, Russian Federation
Duration: 6 Nov 20208 Nov 2020
http://www.iccma.org/

Conference

ConferenceThe 8th International Conference on Control, Mechatronics and Automation
Abbreviated titleICCMA 2020
Country/TerritoryRussian Federation
CityMoscow
Period6/11/208/11/20
Internet address

Keywords

  • RCUK
  • EPSRC
  • EP/R021961/1
  • EP/R022062/1
  • ANN
  • fault detection
  • NPP
  • PWR

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