Abstract 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. 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 & P processes. However, manual video analysis could hardly provide timely information for diagnosing delays. Previous studies show the potentials of using real-time videos for capturing field anomalies. These studies fell short in examining automatic video analysis in compact work environments with significant occlusions. Besides, limited studies revealed how the captured field anomalies trigger delays during outages. Computer vision techniques have the potential for automating field video analysis and detections of prolonged task completions and long waiting times. This paper aims at automating the integrated use of 1) real-time computer vision and spatial analysis algorithms, and 2) data-driven simulations of FO & P processes for supporting predictive outage control. The authors first use the video-based human tracking algorithm to detect human/team behaviors from field videos. Then, the authors formalized detailed human-task-workspace interactions for establishing a simulation model of FO & P processes during outages. The simulation model takes the field anomalies captured from videos as inputs to adjust model parameters for achieving reliable predictions of workflow delays. Major observations show that 1) task delays often occur at the initial stage of the workflow, and 2) waiting line accumulates due to excessive resource sharing during handoffs at the middle stage of the workflow. The simulation results show that tasks on the critical-path are more sensitive to these anomalies and cause up to 5.53% delays against the as-planned schedule.

中文翻译：

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通过计算机视觉和数据驱动的模拟预测核电站停运控制

摘要 核电厂 (NPP) 停运中的现场操作和准备 (FO & P) 过程涉及繁琐的团队协调过程。本研究通过计算机视觉和数据驱动模拟提出了一种预测性核电厂停运控制方法。所提出的方法旨在自动检测异常的人员/团队行为并预测停电期间的延迟。异常的人类/团队行为，例如长时间的任务完成和长时间的等待，可能会导致延迟。及时捕获这些现场异常并准确预测延迟对于指导停电期间的时间表更新至关重要。当前的停电控制在很大程度上依赖于人工观察和基于经验的现场调整，这需要大量的管理工作。捕捉异常人类/团队行为的实时现场视频可以为支持异常 FO & P 流程的预测提供信息。然而，手动视频分析很难为诊断延误提供及时的信息。先前的研究显示了使用实时视频捕捉场异常的潜力。这些研究在检查具有明显遮挡的紧凑工作环境中的自动视频分析方面存在不足。此外，有限的研究揭示了捕获的现场异常如何在停电期间触发延迟。计算机视觉技术具有自动化现场视频分析和检测任务完成时间长和等待时间长的潜力。本文旨在自动化1）实时计算机视觉和空间分析算法的集成使用，2) 数据驱动的 FO 和 P 过程模拟，用于支持预测性停电控制。作者首先使用基于视频的人体跟踪算法从现场视频中检测人员/团队行为。然后，作者将详细的人工-任务-工作空间交互形式化，用于在停电期间建立 FO & P 流程的模拟模型。仿真模型将从视频中捕获的场异常作为输入来调整模型参数，以实现对工作流程延迟的可靠预测。主要观察结果表明：1）任务延迟经常发生在工作流的初始阶段，2）由于在工作流的中间阶段交接过程中过度的资源共享，等待线累积。仿真结果表明，关键路径上的任务对这些异常更敏感，最多可导致 5 个。