Shi Yiguan, Cao Yazhao, Chen Yong, Zhang Longjie
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
China South Industries Group Automation Research Institute Co. Ltd, Mianyang, 621000, China.
Sci Rep. 2024 May 25;14(1):11963. doi: 10.1038/s41598-024-62174-0.
Due to the challenge of collecting a substantial amount of production-quality data in real-world industrial settings, the implementation of production quality prediction models based on deep learning is not effective. To achieve the goal of predicting production quality with limited data and address the issue of model degradation in the training process of deep learning networks, we propose Meta-Learning based on Residual Network (MLRN) models for production quality prediction with limited data. Firstly, the MLRN model is trained on a variety of learning tasks to acquire knowledge for predicting production quality. Furthermore, to obtain more features with limited data and avoid the issues of gradient disappearing or exploding in deep network training, the enhanced residual network with the effective channel attention (ECA) mechanism is chosen as the basic network structure of MLRN. Additionally, a multi-batch and multi-task data input approach is implemented to prevent overfitting. Finally, the availability of the MLRN model is demonstrated by comparing it with other models using both numerical and graphical datasets.
由于在实际工业环境中收集大量生产质量数据面临挑战，基于深度学习的生产质量预测模型的实施效果不佳。为了在有限数据的情况下实现生产质量预测目标，并解决深度学习网络训练过程中的模型退化问题，我们提出了基于残差网络的元学习（MLRN）模型用于有限数据的生产质量预测。首先，在各种学习任务上训练MLRN模型，以获取预测生产质量的知识。此外，为了在有限数据下获得更多特征并避免深度网络训练中梯度消失或爆炸的问题，选择具有有效通道注意力（ECA）机制的增强残差网络作为MLRN的基本网络结构。此外，实施多批次和多任务数据输入方法以防止过拟合。最后，通过使用数值和图形数据集将MLRN模型与其他模型进行比较，证明了该模型的可用性。
