What question did this study set out to answer?

To develop a portable AI diagnostic system for real-time troubleshooting of circuit boards.

February 22, 2026Open Access

AI-Powered Real-Time Circuit Board Diagnostic System: Design, Development, and Implementation

Key Points

To develop a portable AI diagnostic system for real-time troubleshooting of circuit boards.
Combined computer vision and large language models for diagnostics.
Field validation across 12 repair facilities with 342 cases.
Implemented specialized diagnostic modes for various component types.
Enabled continuous monitoring of diagnostic processes.
Achieved 95.3% accuracy in identifying components.
Reduced diagnostic time by 42%.
Lowered error rates from 18% to 3%.
Demonstrated ROI within two weeks of deployment.

Abstract

This paper presents a novel AI-powered diagnostic system for electronics troubleshooting that combines computer vision and large language models in a portable, real-time device. The system achieves 95. 3% component identification accuracy and reduces diagnostic time by 42% while lowering error rates from 18% to 3%. Through field validation across 12 repair facilities and 342 repair cases, we demonstrate the practical viability of AI-assisted diagnostics for electronics technicians. Key contributions include specialized diagnostic modes for different component types, continuous monitoring capability, and empirical validation of human-AI collaboration in technical work. The complete system costs 350-450 and achieves ROI within two weeks of deployment. Keywords: artificial intelligence, computer vision, circuit board diagnostics, large language models, electronics repair, human-AI collaboration, real-time analysis, embedded systems

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