An Open-Source, Autonomous Platform for High-Resolution Energy Monitoring in Manufacturing

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.