Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation
针对先进制造中传感器数据缺失问题，提出一种结合Transformer和选择性生成的生成式对抗插补网络（SGT-GAIN），能捕捉序列信息、减少插补偏差并增强鲁棒性，在数值模拟和增材制造案例中验证了有效性。
Although data have been extensively leveraged for process monitoring and control in advanced manufacturing, it still suffers from the connection issues among sensors, machines, and computers, which may lead to significant data loss, i.e., missing region in the collected data, in the application of data-driven monitoring. To address the missing region issues, one popular way is to perform missing data imputation. With the advances of machine learning, many approaches have been developed for the missing data imputation, such as the popular Generative Adversarial Imputation Network (GAIN), which is based on the Generative Adversarial Network (GAN). However, the inherent shortcomings of generative adversarial architecture may still lead to unstable training. More importantly, the collected online sensor data in manufacturing are in sequential order whereas GAIN considered the input data independently. Hence, to address these two limitations, this work proposes a novel approach termed transformer-enabled GAIN with selective generation (SGT-GAIN). The contributions of the proposed SGT-GAIN consist of three aspects: (i) the architecture for transformer-enabled generation is developed to capture the sequential information among the data; (ii) a selective multi-generation framework is proposed to further reduce the imputation bias; and (iii) an ensemble learning framework is applied to enhance the imputation robustness. Both the numerical simulation study and a real-world case study in additive manufacturing demonstrated the effectiveness of the proposed SGT-GAIN.