Convolutional Generative Adversarial Network-based Flash Welding Quality Inspection Method for Anchor Chains

With the rapid growth of the shipbuilding industry, anchor chain flash welding quality inspection has become a critical concern. Due to the challenge of collecting abnormal state data, most anchor chain samples are normal, making anomaly detection from limited anomalous data a key research problem. Therefore, this paper establishes the LSAC-DPP GAN model based on Least Squares Generative Adversarial Networks (LS GAN) by adding a Determinantal Point Process (DPP)-based extra loss term to the generator's loss function. From a semi-supervised perspective, the LSAC-DPP GAN learns normal and abnormal samples, enabling the model to avoid generating abnormal samples explicitly. The problem of pattern collapse that exists in the model is mitigated by adding a loss term based on DPP to the generator's loss function, allowing the generator to generate samples with better diversity. Finally, we trained the LSAC-DPP GAN with electrode position and current signal data to generate normal displacement and signal data. High recall, accuracy, and F1 scores were obtained on the test set.