Automatic crane-related workflow control for nuclear plant outages through computer vision and simulation

Abstract

Nuclear power plant (NPP) outages involve a large number of maintenance activities with a tight schedule and zero-tolerance for accidents. Outage projects thus need real-time control to ensure safety and productivity. During outages, crane lifting is critical for outage control and risk management. An effective outage control method should monitor detailed interactions between human and workspaces, and streamline the workflows of cranes to control both productivity and risks. Unfortunately, current approaches of outage control rely heavily on tedious and error-prone manual inspection that can hardly achieve detailed spatiotemporal monitoring. This paper presents an automated outage control framework that enables detailed human behavior analysis, automatic comparison of as-planned and actual crane-related operations, and effective decision-making for crane-related workflow control. In this framework, a real-time human tracking algorithm uses 2D/3D imagery to automatically derive the status of workspaces (e.g., waiting, active). Then a change-analysis algorithm detects and diagnoses differences between as-is workflow information against as-planned schedules, and thus enables field managers to implement a close-loop outage control. Preliminary results indicate the potential of this integrated outage control in improving the safety, productivity, and quality of outages, as well as outage project planning.