Field operation and preparation (FO & P) processes in the outages of nuclear power plants (NPPs) involve tedious team coordination processes. This study proposed a predictive NPP outage control method through computer vision and data-driven simulation. The proposed approach aims at automatically detecting abnormal human/ team behaviors and predicting delays during outages. | Progress in Nuclear Energy 127 2020 103448 Contents lists available at ScienceDirect Progress in Nuclear Energy journal homepage http locate pnucene Predictive nuclear power plant outage control through computer vision and data-driven simulation Zhe Sun a Cheng Zhang b Jiawei Chen a Pingbo Tang c Alper Yilmaz d a School of Sustainable Engineering and the Built Environment Arizona State University 660 S College Avenue Tempe AZ 85281 USA b The Zachry Department of Civil Engineering Texas A amp M University 201 Dwight Look Engineering Building College Station TX 77843 USA c Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA d Department of Civil Environmental and Geodetic Engineering The Ohio State University 470 Hitchcock Hall 2070 Neil Avenue Columbus OH 43210 USA A R T I C L E I N F O A B S T R A C T Keywords Field operation and preparation FO amp P processes in the outages of nuclear power plants NPPs involve tedious Nuclear power plant outage team coordination processes. This study proposed a predictive NPP outage control method through computer Computer vision vision and data-driven simulation. The proposed approach aims at automatically detecting abnormal human Simulation team behaviors and predicting delays during outages. Abnormal human team behaviors such as prolonged task completion and long waiting time could induce delays. Timely capturing these field anomalies and precisely predicting delays is critical for guiding schedule updates during outages. Current outage control relies heavily on manual observations and experience-based field adjustments which require extensive management efforts. Real- time field videos that capture abnormal human team behaviors could provide information for supporting the prognosis of abnormal FO amp P processes. However manual video analysis could hardly provide timely infor mation for diagnosing delays. Previous studies show the potentials
