An novel circuit board fault diagnosis network using infrared thermal image

Abstract In practical industrial applications, infrared thermography fault diagnosis has received widespread attention. In order to detect the internal component faults of circuit boards, this paper proposes an novel circuit board fault diagnosis network using infrared thermal image.Firstly, a network LGC-Net network is proposed to improve ResNet50, and a two-layer attention module and a channel feature extraction module are introduced into the ResNet50 network, which strengthens the network's ability to extract global features and improves the model's extraction of features such as multi-scale key detail features of the input feature maps and the target contour, etc. Optimization of the residual structure and model pre-training plus fine-tuning are used for the Diagnostic model construction, so that the accuracy of fault diagnosis has been greatly improved. Finally, the acquired board infrared thermography dataset is preprocessed and experimented with a diagnostic fault network Experiments show that the LGC-Net network proposed in this paper has a fault diagnosis accuracy of 98.92%, which is 1.9% higher than the optimal accuracy of the classical network fault diagnosis, while the diagnosis time of a single infrared thermography is 246ms, which is 299ms lower than the shortest time consumed by other models for diagnosis.The experimental results show that the method proposed in this paper is able to efficiently identify infrared thermography faults of circuit board components, to improve the accuracy of circuit board fault diagnosis.