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

Title data

Federl, Andreas ; Böhmisch, Markus ; Sagstetter, Valentin ; Fischerauer, Gerhard ; Bösnecker, Robert:
Resource-Efficient Telemetry-Based Condition Monitoring with Digitally Configurable DC/DC Converters and Embedded AI.
In: Electronics. Vol. 15 (2026) Issue 4 . - 852.
ISSN 2079-9292
DOI: https://doi.org/10.3390/electronics15040852

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Digitally configurable power supply; condition monitoring; load monitoring; operational data analysis; machine learning; embedded artificial intelligence
Institutions of the University:	Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Research Institutions > Research Units > Zentrum für Energietechnik - ZET
Result of work at the UBT:	Yes
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited:	24 Feb 2026 08:35
Last Modified:	24 Feb 2026 08:35
URI:	https://eref.uni-bayreuth.de/id/eprint/96379