Development of a Scalable, Risk-informed, Predictive Maintenance Cloud based Strategy at Nuclear Power Plants

Abstract

The fact that light-water reactor operations and maintenance costs are prohibitively expensive and contribute to the premature decommissioning of nuclear power plants (NPPs) is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology, as its low cost, computing and storage adaptability, and ability to host applications across numerous virtual infrastructures potentially make it a cost-effective alternative to onsite storage and diagnostics. In this paper, a technological assessment is carried out on a provisional cloud deployment architecture for a NPP predictive monitoring system. This cloud-based monitoring system would enable maintenance and diagnostic analysts and other authorized plant users to remotely monitor equipment functionality, thus enabling early fault detection and effective predictive maintenance (PdM) practices. To provide data processing and storage, sensor device networking, and database management, the Microsoft Azure cloud platform is utilized as part of the proposed cloud architecture; however, this analysis could be extended to other cloud computing service providers as well. The focus of this paper is on application of cloud resources for enabling PdM, identification of the technological hurdles associated with moving to a cloud-computing-based architecture, and the potential benefits of moving to a centralized cloud system.