Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants
Publication Date
1-1-2024
Document Type
Article
Publication Title
Nuclear Technology
DOI
10.1080/00295450.2024.2372217
Abstract
The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.
Keywords
Explainable artificial intelligence, human error, multiclass classification, nuclear power plants, uncertainty quantification
Department
Computer Science
Recommended Citation
Bhavya Reddy, Ezgi Gursel, Katy Daniels, Anahita Khojandi, Jamie Baalis Coble, Vivek Agarwal, Ronald Boring, Vaibhav Yadav, and Mahboubeh Madadi. "Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants" Nuclear Technology (2024). https://doi.org/10.1080/00295450.2024.2372217
