A Non-Contact PCB Multi-Fault Diagnosis Algorithm Based on Scalar Magnetic Field Fusion Feature and Transformer Architecture

Non-contact printed circuit board (PCB) fault diagnosis has been widely applied in PCB detection and maintenance. Due to objective factors such as visual blind spots, low-loss circuit structure design, and frequency insensitivity, traditional algorithms based on visual and temperature features are limited in practice. Therefore, the algorithm based on electromagnetic features containing rich physical connotations and prominent frequency features has received attention in PCB fault diagnosis. Based on the basic principles of electromagnetic physics and PCB fault relationship, this article proposes a scalar magnetic field source feature and further improves feature performance by adding topological relationships of multi-faults to generate the fusion feature. The backbone of the PCB diagnosis model is established on the Transformer architecture, effectively utilizing self-attention and parallel computing mechanisms to explore the inner correlation between each group feature. The paper provides a new non-contact PCB fault diagnosis solution that enriches existing methods. Besides, through actual experiments setting up multi-fault PCBs, the feasibility of our process is proved based on the proposed features and models. The specific multiple indicators Overall Precision (OP), Per Class Precision (CP), Overall Recall (OR), Per Class Recall (CR), Overall F1 Measure (OF1), Per Class F1 Measure (CF1), Accuracy (ACC), Mean Average Precision (mAP) are 98.55%, 94.89%, 98.55%, 95.11%, 98.55%, 95.32%, 96.01%, and 97.27%