Explainable machine-learning tools for predictive maintenance of circulating water systems in nuclear power plants

Predictive maintenance (PdM) is one of the strategies that has shown great potentials in achieving substantial cost savings and enhancing the economic competitiveness of nuclear power plants in the current energy market. PdM strategy taking advantage of advancements in machine learning (ML) technologies have demonstrated ability in handling high dimensional and multivariate data and in extracting hidden relationships within data in industrial environments. While ML technologies show great potentials, their lack of explainability, especially in considering multiple aspects in human-scale explanation-giving tasks, is one of the major hurdles to their adoptions. The research presented in this paper develops an explainable ML solutions by accounting for four attributes of explainable artificial intelligence, including the contextual factors, explainable model options, post-hoc explanations for black-box models using Shapley additive explanations and local interpretable model-agnostic explanations, and graphical user interface for human cognitive capacity and limitations. This tool is then applied to the conducting of PdM tasks for a circulating water system in a nuclear power plant. • Adopts XAI approach for the development of ML-based diagnostics for NPP CWS. • Investigates trade-offs between model performance and explainability. • Applies SHAP and LIME for post-hoc explanations of black-box models. • Creates a GUI for enhancing model transparency and increasing user trust.