Artificial Intelligence Enabled Predictive Maintenance Digital Twins for Nuclear Power Plant Assets

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The nuclear industry is capital-intensive, heavily regulated, and expected by the public to achieve a perfect safety record. This requires greater safe operations of crucial assets with greater availability. As such the creation of a digital environment to support the design and operation of nuclear facilities is becoming the next logical step. This will provide benefits in terms of reducing the requirements for expensive physical mock-ups and test. In addition, it will provide increasing return on investment through more efficient operation and improving public perception.This purpose of this project is to develop an effective asset management framework. This will enable data-driven decision making at all levels of planning, maintenance, and operations. The integrated models will be leveraged to inform operators of current operational system health and performance metrics. The digital twin (DT) has been identified as a promising approach for deploying these models to operating assets. In this project high performance computing, sensors' data and mathematical modelling will be integrated in a digital model (digital twin). The digital twin will be a computational representation of the asset (nuclear steam generators, valve, pumps). The digital twin consists of a computer-aided engineering (CAE) model of the asset onto which is superimposed data acquired from structural health monitoring during operation and from maintenance periods. Digital twins powered by AI will be exploited to predict impending asset failure and the contributory factors. The system will be able to Digital prescribe operational and/or maintenance actions to preserve equipment health so that can plant downtime can be minimized.