Tang Peihao, Li Zhen, Wang Xuanlin, Liu Xueping, Mou Peng
Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2025 Jan 16;25(2):493. doi: 10.3390/s25020493.
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data.
预测制造过程的时间序列能耗数据可以优化能源管理效率，并降低企业的维护成本。使用深度学习算法为传感器数据建立预测模型是一种有效的方法；然而，这些模型的性能受到训练数据的数量和质量的显著影响。在实际生产环境中，制造过程中可收集的时间序列数据量有限，这可能导致模型性能下降。在本文中，我们使用改进的TimeGAN模型对能耗数据进行增强，该模型在恢复模型中纳入了多头自注意力机制层，以提高预测精度。使用混合CNN-GRU模型预测制造设备运行过程中的能耗数据。经过数据增强后，预测模型的RMSE和MAE显著降低，R值增加。当生成的合成数据量约为原始数据量的两倍时，模型的预测精度达到最大化。
