Integrated Component Health Monitoring and Identification System Utilizing Raspberry Pi with Thermal and Visual Imaging for Fault Detection and Analysis

The complexity of electronic systems is increasing, therefore it's critical to keep an eye out for any problems with their individual parts. This study provides a method to identify and assess the condition of electronic components using thermal imaging, visual imaging, and a Raspberry Pi. Thermal analysis and component identification are the two primary phases of the system's operation. Using standard photos, a deep learning model is initially trained to identify and label various circuit board components. Thermal pictures are employed in the second stage to identify anomalous temperature patterns that may be signs of issues. This technology assists in locating malfunctioning components and helps identify them by integrating thermal and visual data. Tests on genuine circuit boards show that the approach is functional and can be used for tracking in numerous digital applications.