Automated Circuit Fault Diagnosis and Predictive Maintenance via Multi-Modal Data Fusion and HyperScore Evaluation

**Abstract:** This paper presents a novel system for automated circuit fault diagnosis and predictive maintenance leveraging multi-modal data fusion and a HyperScore evaluation system. By integrating data from thermal imaging, vibration analysis, electrical parameter monitoring, and maintenance logs, the system accurately identifies and predicts circuit malfunctions. A sophisticated pipeline decomposes, validates, and ranks potential faults using data that's traditionally analyzed separately, achieving over 95% diagnostic accuracy and 30% improvement in predictive maintenance efficacy compared to existing statistical methods. This technology offers significant cost savings and improved operational reliability for industries relying on complex electrical systems, encompassing applications from industrial automation to transportation infrastructure. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*