Nuclear power generation will be key to meeting carbon free energy transition goals. However, nuclear power must provide agility and flexibility to fluctuating power demands when other sources of carbon-free energy like solar and wind may not be as flexible. This has led to small modular reactor (SMR) development where nuclear power will be generated from a distributed fleet of smaller reactors where units can be brought online or offline as needed. Due to its high operational and maintenance (O&M) costs as it is, a distributed fleet will put additional cost burden if remote monitoring and crew sharing is not enabled. This requires prognostics and health management (PHM) capabilities such as early warning, diagnostics, and prognostics to enable predictive maintenance with high accuracy. Typically, monitoring solutions are developed on component and subsystem levels targeting specific failure modes. However, it is argued that a systemwide monitoring, in addition to specific targeted analytics, would be of key importance. This paper presents a deep-causal unsupervised anomaly detector that has been successfully applied in various aerospace and renewable energy applications. In this paper we share our experience applying this method on a nuclear power plant (NPP) application. Specifically, we share how we dealt with practical challenges of data quality, ground truth labeling, performance evaluation and field validation in an unknown-unknown setting where prior knowledge of failures and failure modes were not available to begin with.
How to Cite
Deep learning, Causal Learning, Anomaly detection, unsupervised, nuclear plant, PHM, digital twin, maintenance, O&M cost, SMR, Small Modular Reactor
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