Resource-Efficient Telemetry-Based Condition Monitoring with Digitally Configurable DC/DC Converters and Embedded AI

Digitally configurable DC/DC converters provide built-in telemetry signals that offer new opportunities for operational data-driven monitoring in embedded energy systems. However, exploiting these signals for intelligent condition monitoring remains challenging due to limited computational resources and the need to preserve the safety and determinism of power supply control. This work investigates the combination of digitally configurable DC/DC converters and embedded artificial intelligence for resource-efficient load and condition monitoring based exclusively on converter-side power telemetry. A lightweight, feature-based current analysis pipeline is proposed, incorporating domain-informed temporal and electric features. Three representative machine learning model classes, Random Forest, Support Vector Machine, and a Neural Network, are evaluated. The approach is implemented on an ESP32-class microcontroller operating as a dedicated monitoring unit, fully separated from the safety-critical power supply control. Experimental validation on a laboratory demonstrator shows that classification accuracies of up to 99% can be achieved for four system states using only five features at a 100 Hz telemetry sampling rate, while remaining within typical embedded memory constraints. The results demonstrate that converter-internal telemetry enables effective and scalable condition monitoring without additional sensors, supporting the combination of embedded intelligence and digitally configurable power supplies for industrial applications.