基于TVA-TCN的制造过程关键参数多步预测方法
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作者单位:

中国科学院沈阳自动化研究所

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中图分类号:

TP391;TP273

基金项目:

辽宁省重点研发计划项目


Multi-step prediction method for key parameters of manufacturing process based on TVA-TCN
Author:
Affiliation:

Shenyang Institute of Automation Chinese Academy of Sciences

Fund Project:

Liaoning Provincial Key Research and Development Program

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    摘要:

    制造过程关键参数的准确预测对制造过程的精确控制起关键作用,现有预测方法通常未考虑时间动态特性,多步预测性能不佳,无法满足制造过程实际需求。针对上述问题,提出一种基于时变注意力时间卷积网络(Time-Varying Attention-Temporal Convolutional Network,TVA-TCN)的制造过程关键参数多步预测方法。首先,鉴于普通卷积网络感受野的局限性,利用多通道时间卷积网络提取数据的长期依赖关系,并使用Softplus激活函数降低对数据异常值的敏感度;其次,提出一种时变模型结构,通过提取上一时间步的隐藏层信息和输出信息,使得模型不仅能够随时间动态更新,而且可以缓解梯度消失,从而提高多步预测性能;最后,利用食品加工制造过程的实际数据进行多步预测实验,结果表明所提方法与传统的方法相比具有明显的优势。

    Abstract:

    Accurate prediction of key parameters in manufacturing process plays a key role in its precise control. Existing prediction methods usually do not consider time dynamic characteristics, and the performance of multi-step prediction is not good, which cannot meet the actual needs of manufacturing process. In response to the above problems, a multi-step prediction method for key parameters of manufacturing process based on Time-Varying Attention-Temporal Convolutional Network (TVA-TCN) is proposed. First, in view of the limitations of the receptive field of ordinary convolutional network, multi-channel temporal convolutional network is used to extract the long-term dependence of data, and the Softplus activation function is used to reduce the sensitivity of data outliers. Then, a time-varying model structure is proposed, by extracting the hidden layer information and output information of the previous time step, the model can not only be dynamically updated over time, but also can alleviate the disappearance of gradients, thereby improving the performance of multi-step prediction. Finally, multi-step prediction experiments were carried out using the real data of food processing process, and the results showed that this method has obvious advantages compared with the traditional method.

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历史
  • 收稿日期:2021-05-25
  • 最后修改日期:2021-09-19
  • 录用日期:2021-09-22
  • 在线发布日期: 2021-11-01
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