Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation
Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation
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DOI：
10.1080/24725854.2023.2193257
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发表时间：
2023-03
影响因子：
2.6
通讯作者：
Yuxuan Li;Zhangyue Shi;Chenang Liu
中科院分区：
文献类型：
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作者：
Yuxuan Li;Zhangyue Shi;Chenang Liu
摘要虽然数据在先进制造中已被广泛用于过程监控，但在数据驱动的监控应用中，数据仍然存在传感器、机器和计算机之间的连接问题，这可能导致重大的数据丢失，即采集数据中的缺失区域。为了解决缺失区域的问题，一种流行的方法是执行缺失数据补偿。随着机器学习的发展，人们提出了许多方法来弥补缺失数据，如流行的基于生成性对抗性网络的生成性对抗性推算网络(Gain)。然而，生成性对抗性结构的固有缺陷仍可能导致训练的不稳定。更重要的是，在制造业中收集的在线传感器数据是按顺序排列的，而Gain独立地考虑了输入数据。因此，为了解决这两个局限性，本工作提出了一种新的方法，称为选择性产生变压器使能增益(SGT-Gain)。所提出的SGT-GAIN的贡献包括三个方面：(1)开发了变压器使能生成的体系结构以捕获数据之间的顺序信息；(2)提出了选择性多生成框架以进一步减少补偿偏差；(3)应用集成学习框架来增强填充的稳健性。数值模拟研究和加法制造的实际案例研究都证明了所提出的SGT-Gain算法的有效性。
Abstract 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.