Predictive Maintenance of Critical Systems & Components in Canada's Nuclear Energy Infrastructure Under Seismic Risk

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Two of the main challenges facing safe nuclear power plants (NPPs) operations are: 1) Beyond design-basis seismic hazards; and 2) Interdependence-induced complexity of critical, safety related, systems and components within NPPs. For rock sites in eastern North America, design response spectra recommended by the Canadian Standard Association and the US Nuclear Regulatory Commission are unsafe for frequencies above 17 Hz and overly conservative for lower frequencies. This situation poses a significant threat for Canada, because its nuclear infrastructure is clustered in the eastern provinces of Ontario, Québec, and New Brunswick, while current design recommendations are based on western seismological records; especially considering the high likelihood that Canada will experience a seismic event(s) in the near future. The second NPPs safe operation challenge pertains to the functionality of safety related systems and components, which form an intricate complex system of interdependent components that operates in a manner often understood only at a component-level, while its system-level behaviour is often concealed to the operators. Plant managers are currently unable to provide robust predictions of (system-level) systemic failures, wherein relatively minor damage of a handful of components may lead to a cascade of consequences culminating in complete (and often unsafe) NPP shutdown. In this context, motor operated valves (MOVs) are a primary concern, given how central to the safe operation of NPPs they are. A greater understanding of MOV interdependent networks is key to predict and prevent future catastrophic events. In order to mitigate the risk of service interruption, this research will focus on the development of intelligent tools designed to aid NPP operators in the challenging task of predictive maintenance. A tri-level model will be developed to predict the behaviour of MOVs and MOV network when excited by ground motion corresponding to Canadian NPP sites. Based on the model output, plant managers will be able to prioritize future intervention of maintenance and functional optimization, to minimize the duration, frequency and overall cost of power outages. In Focus Area I, the study will be limited to MOV networks in original condition; in Focus Area II, the model will be extended to incorporate the concurrent and compounding effects of MOV common cause failures and ground shaking, and the results will be validated via shake table testing; in Focus Area III, risk-based monitoring tools will be developed to facilitate the proposed MOV network predictive maintenance tool. This research program will bolster safety and augment productivity in the nuclear industry-a sector that generates $6 billion in revenues every year and provides 30,000 direct jobs, while contributing to the tutelage of the environment via low GHG emissions-by reducing the costs associated with the interruption of service, maintenance, and infrastructure renewal.