复杂工业流程中的多地平线 KPI 预测:带有部分教师强迫的自适应编码器-解码器框架

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Hu Zhang;Zhaohui Tang;Yongfang Xie;Zhoushun Zheng;Weihua Gui
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引用次数: 0

摘要

关键绩效指标(KPI)反映了制造操作的质量和效率,KPI预测可以在许多工业过程中实现适当的操作或控制。然而,现有的KPI预测方法不足以有效地管理跨多周期KPI的提前预测,从而阻碍了复杂工业过程的精确和及时控制。因此,我们提出了一个具有部分教师强制策略(PTF-ED)的自适应编码器-解码器框架,以实现灵活的多水平KPI预测。首先,我们使用一个编码器来处理输入时间序列和一个注意层来生成上下文向量。然后,我们将测量到的kpi分为延迟时间序列和当前时间序列,并依次设计一个延迟解码器和一个电流解码器,使延迟时间序列和当前时间序列对应于输入时间序列。特别是,我们提出了一种部分教师强迫策略,以有效地利用测量的KPI,并解决当前时间序列中训练和推理阶段之间的暴露偏差的挑战。此外,我们在模型训练损失中引入了加权多水平预测约束,以约束不同样本区间的输入输出对应关系。通过数值模拟研究和实际锌浮选过程的实例研究,验证了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multihorizon KPI Forecasting in Complex Industrial Processes: An Adaptive Encoder–Decoder Framework With Partial Teacher Forcing
Key performance indicator (KPI) reflects the quality and efficiency of manufacturing operations, and KPI forecasting enables proper operations or controls in many industrial processes. However, existing KPI forecasting methods are inadequate for managing the advance prediction of KPI across multiple cycles effectively, which impedes precise and timely control in complex industrial processes. Therefore, we propose an adaptive encoder-decoder framework with partial teacher forcing strategy (PTF-ED) to enable flexible multihorizon KPI forecasting. First, we employ an encoder that processes the input time series and an attention layer to generate the context vectors. Then, we divide the measured KPIs into delayed and current time series, and design a delayed decoder and a current decoder in series to make the delayed and current time series correspond to the input time series. Especially, we propose a partial teacher forcing strategy to utilize the measured KPI efficiently and tackle the challenge of exposure bias in the current time series between training and inference phases. Moreover, we introduce a weighted multihorizon forecasting constraint in the model training loss to constrain the input-output correspondence across different sample intervals. The effectiveness of the proposed model has been validated through both a numerical simulation study and a case study in a real-world zinc flotation process.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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