一个用于制造业高分辨率能源监测的开源自主平台
An Open-Source, Autonomous Platform for High-Resolution Energy Monitoring in Manufacturing
中文总结 AI 辅助
针对工业4.0中高分辨率能源数据获取难题，提出开源自主的AEMS平台，结合研究级保真度与工业可部署性，通过特定架构和接口实现多机器采集，经实验验证其有效性，推动高保真能源监测普及。
中文摘要 AI 辅助
高分辨率能源数据在工业4.0中愈发关键，三相电压和电流等电信号蕴含机器状态、刀具磨损及过程动态的丰富信息，但实际获取困难。本文提出自主能源监测系统（AEMS），它是开源、低成本且模块化的平台，由主机、边缘网关和可选云软件栈支持，能独立于持续连接主机进行自主、长时间采集。系统通过隔离前端和24位同步采样模数转换器采集三相电压和电流，由双核架构管理。通过工业接口和硬件级同步实现跨多台机器的可扩展、时间对齐采集。在三轴CNC加工中心验证该平台，可解析主轴、进给驱动等能量状态并检测小至50mm/min的进给速率变化。通过公开完整硬件和固件，旨在让研究人员及中小企业制造商都能获取高保真能源监测。
英文摘要
High-resolution energy data is increasingly central to Industry 4.0, where electrical signals such as three-phase voltage and current carry rich information about machine condition, tool wear, and process dynamics. Capturing this information in practice remains difficult: commercial power analysis are largely proprietary, offer limited or no access to high-sampling rate data for transient analysis, restrict access to raw waveform data, and offer no customization, while general-purpose open hardware lacks the front-end accuracy, isolation, and robustness required for industrial measurement. This paper presents Autonomous Energy Monitoring System (AEMS), an open-source, low-cost, and modular platform supported by a host, edge-gateway, and optional cloud software stack that enables autonomous, long-duration acquisition independent of a continuously connected host and thereby closes this gap by combining research-grade fidelity with industrial deployability. The system acquires three-phase voltage and current through an isolated front-end and a 24-bit, simultaneously sampling analog-to-digital converter, managed by a dual-core architecture that separates deterministic acquisition and on-board logging from host communication and control. Industrial interfaces (Ethernet, RS-485/Modbus, and BLE) together with hardware-level synchronization enable scalable, time-aligned acquisition across multiple machines, supported by a complete host, edge-gateway, and optional cloud software stack. We validate the platform on a three-axis CNC machining center, where it resolves spindle, feed-drive, rapid-traverse, and material-removal energy states and detects feed-rate changes as small as 50 mm/min. By releasing the full hardware and firmware openly, this work aims to democratize access to high-fidelity energy monitoring for both researchers and small and medium-sized manufacturers.
