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首页> 外文期刊>Automatica Sinica, IEEE/CAA Journal of >Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input
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Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input

机译：基于变分推理的内核动态贝叶斯网络，用于构建工业时间序列的预测间隔与不完整输入

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摘要

Prediction intervals (PIs) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks (KDBN), serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference. This study proposes a variational inference method for the KDBN for the purpose of fast inference, which avoids the time-consuming stochastic sampling. The proposed algorithm contains two stages. The first stage involves the inference of the missing inputs by using a local linearization based variational inference, and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices. To verify the effectiveness of the proposed method, a synthetic dataset and a practical dataset of generation flow of blast furnace gas (BFG) are employed with different ratios of missing inputs. The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.
机译：工业时间序列的预测间隔（PIS）可以为工人提供有用的指导。鉴于工业传感器的失败可能导致输入中的缺失点，现有的内核动态贝叶斯网络（KDBN）作为PIS构建的有效方法，使用随机推断的随机算法遭受高计算负荷。本研究提出了KDBN的变分推理方法，以便快速推断，避免了耗时的随机取样。所提出的算法包含两个阶段。第一阶段涉及通过使用基于本地线性化的变化推断，并且基于缺失输入的计算后的后部分布，第二阶段在未来的时间切片中看到高斯近似以用于节点上的概率。为了验证所提出的方法的有效性，用缺失输入的不同比例使用合成数据集和产生的高炉气体的生成流动的实际数据集。实验结果表明，该方法可以为BFG的生成流提供可靠的PIS，并且它表现出比随机基于随机的计算时间更短。

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  • 来源
    《Automatica Sinica, IEEE/CAA Journal of》 |2020年第5期|1437-1445|共9页
  • 作者

    Chen Long; Wang Linqing; Han Zhongyang; Zhao Jun; Wang Wei;

  • 作者单位

    Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China|Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China;

    Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China|Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China;

    Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China|Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China;

    Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China|Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China;

    Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China|Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Industrial time series; kernel dynamic Bayesian networks (KDBN); prediction intervals (PIs); variational inference;

    机译：工业时间系列;内核动态贝叶斯网络（KDBN）;预测间隔（PIS）;变分推论;

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