NDLI: Machine learning based fault prediction system for the primary heat transport system of CANDU type pressurized heavy water reactor

Content Provider	IEEE Xplore Digital Library
Author	Ahsan, S.N. Hassan, S.A.
Copyright Year	2013
Description	Author affiliation: Karachi Inst. of Nucl. Power Eng., Karachi, Pakistan (Hassan, S.A.) \|\| Comput. Dev. Div., KANUPP Paradise Point, Karachi, Pakistan (Ahsan, S.N.)
Abstract	In nuclear power reactor, temperature of the core has to be maintained within the safety limits. This can be achieved by monitoring and controlling the various system's parameter of nuclear reactor. One of the important system of nuclear reactor is primary heat transfer (PHT) system. Therefore, any fault in the PHT system may lead to the state where PHT parameters cross the safety limits, and reactor becomes unsafe for operation. To avoid such conditions various fault monitoring and controlling systems have been used in nuclear power reactors. In the recent years, machine learning techniques have been used to build automatic fault prediction system which can be used as a fault monitoring system of nuclear power plant. In this paper, we propose our approach to build machine learning based fault prediction system for the PHT system of CANDU (Canada Deuterium Uranium) type reactor. The proposed approach is based on the classification techniques of supervised machine learning. Whereas, to validate our approach, we performed an experiment by extracting the historical data of the following reactor's parameters: coolant flow rate, coolant header temperature, and neutron power rate. After extracting the parameter's data, we labeled the data with the following three plant statuses: running, transient and shutdown. Finally, we used the binary tree and artificial neural network techniques of machine learning and built models which successfully classified the three statuses of the plant. In our experiment, the maximum obtained accuracy of our model is 99%. It shows that our proposed system can be used to predict fault in PHT loop.
Sponsorship	IEEE Lahore Sect. C Chapt.
Starting Page	68
Ending Page	74
File Size	326131
Page Count	7
File Format	PDF
e-ISBN	9781479920464
DOI	10.1109/ICOSST.2013.6720608
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature measurement Artificial neural networks Formatting Coolants Style Fuels Data mining Insert Inductors Transient analysis Styling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in