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Grant: Collaborative Research and Development Grants

University of Ontario Institute of Technology

$18,511.00

Intelligent Experience Retention System (IERS)_x000d_

EF 2020-21

Résumé
In nuclear power plants (NPP), and at OPG, due to continuous moves of experienced personnel to different department or retirements, a vast amount of expertise and systems-specific knowledge is lost. This leads to longer training periods for new employees and sometimes to delayed responses to problems. The loss of expertise costs OPG a large amount of money as they have to invest in training less experienced staff, and leads to indirect losses in delayed or wrong activities, in particular in reactor maintenance and inspection activities. Moreover, the unavailability of expert systems limited the ability of staff to perform their tasks effectively. Another challenge and need to retain engineering knowledge is specific activities such as refurbishment or decommissioning, which are repeated over a long period of time (20-50 years) which did not enable building expertise in these areas, and transferring expertise became important. The challenges include: retirement of expertise in NPP; transformation and diversity of technologies in plant operation and maintenance; advancement in communications between human-human, machine-human, brain-machine, and machine-machine; and expensive and time consuming training of young generation plant engineers and personal. This project is aiming at the design of novel next generation operation experience support system by learning from engineering and operational data, as well as human experience and build comprehensive knowledgebase to support critical operation activities in timely manner. The project will demonstrate the development of Intelligent Experience Retention System (IERS) to be demonstrated with a number of case studies within OPG, prior to wide deployment in a number of sections. The project will include conceptual design, implementation, testing and AFS (Available For Service) of the IERS as a self-learning automated intelligent system that retains the personnel expertise, analyses it, and makes it ready to answer related questions asked by inexperienced employees.
Programme
Collaborative Research and Development Grants
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
542541-2019
Name nom
Gaber, Hossam
Department département
Head Office
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
18511
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
800
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information and communication services
Area of application fr
Services d'information et de télécommunications
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:2 - 2
Partie
1
Nb partie
2
Application title
Intelligent Experience Retention System (IERS)_x000d_
Keyword
NSERC
Application summary
In nuclear power plants (NPP), and at OPG, due to continuous moves of experienced personnel to different department or retirements, a vast amount of expertise and systems-specific knowledge is lost. This leads to longer training periods for new employees and sometimes to delayed responses to problems. The loss of expertise costs OPG a large amount of money as they have to invest in training less experienced staff, and leads to indirect losses in delayed or wrong activities, in particular in reactor maintenance and inspection activities. Moreover, the unavailability of expert systems limited the ability of staff to perform their tasks effectively. Another challenge and need to retain engineering knowledge is specific activities such as refurbishment or decommissioning, which are repeated over a long period of time (20-50 years) which did not enable building expertise in these areas, and transferring expertise became important. The challenges include: retirement of expertise in NPP; transformation and diversity of technologies in plant operation and maintenance; advancement in communications between human-human, machine-human, brain-machine, and machine-machine; and expensive and time consuming training of young generation plant engineers and personal. This project is aiming at the design of novel next generation operation experience support system by learning from engineering and operational data, as well as human experience and build comprehensive knowledgebase to support critical operation activities in timely manner. The project will demonstrate the development of Intelligent Experience Retention System (IERS) to be demonstrated with a number of case studies within OPG, prior to wide deployment in a number of sections. The project will include conceptual design, implementation, testing and AFS (Available For Service) of the IERS as a self-learning automated intelligent system that retains the personnel expertise, analyses it, and makes it ready to answer related questions asked by inexperienced employees.