PCB Fault Identification With Multiscale Feature Fusion and Heat Source Attention Based on Multimodal Infrared Thermal Imaging

The widespread use of complex-structured printed circuit boards (PCBs) in high-tech equipment has increased reliability and fault detection demand. Low resolution and significant differences in infrared thermal imaging feature scales limit the application in PCB fault identification using existing deep learning models. A PCB fault identification framework based on multimodal infrared thermal imaging multiscale feature fusion and heat source attention (HSA) is proposed to address this challenge. The framework significantly enhances the accuracy and robustness of fault detection by encoding multiscale features from multimodal infrared thermal images. In the proposed framework, the input images are extended into RST multimodal infrared thermal images, which include the spatiotemporal variation rate of the temperature field, enriching the thermal image feature. Additionally, the framework integrates feature pyramid networks (FPN) and HSA modules into the encoding network, improving the ability to express fault-related features. Experimental validation on a two-phase drive (TPD) circuit demonstrates that the proposed framework improves classification accuracy by 4.0% compared to existing deep convolutional neural networks (CNN), showing high robustness against focus blur and pixel failures. More importantly, the framework accurately detects faults at early stages. This study improves the accuracy and efficiency of PCB fault identification and provides technical support for the reliability monitoring of complex electronic equipment.