Experiments Dataset for PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models

Contains data supporting the IEEE Access submission titled "PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models".Paper's Abstract:We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling highly accurate digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam’s ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics.

本数据集包含支撑已投至IEEE Access的论文《PerfCam：基于三维高斯溅射（3D Gaussian Splatting）与视觉模型的产线数字孪生》的相关数据。论文摘要如下：本研究提出PerfCam——一款开源概念验证（Proof-of-Concept，PoC）数字孪生框架，该框架将摄像头与传感数据、三维高斯溅射（3D Gaussian Splatting）技术及计算机视觉模型相结合，可应用于工业产线的数字孪生构建、目标追踪与关键绩效指标（Key Performance Indicators，KPIs）提取。PerfCam借助三维重建技术与卷积神经网络（Convolutional Neural Networks，CNNs），为目标追踪与空间映射提供半自动化方案，可构建高精度数字孪生模型，实时采集产线的设备可用率、性能表现、设备综合效率（Overall Equipment Effectiveness，OEE）以及传送带运行速率等关键绩效指标。本研究通过在制药行业的真实测试产线中开展实际部署，验证了PerfCam的有效性，并公开发布本数据集以支撑该领域后续的研究与开发工作。实验结果表明，PerfCam可凭借其高精度数字孪生功能提供可落地的决策洞察，凸显了其作为智能制造环境中构建实用数字孪生模型、提取运营分析数据的有效工具的价值。