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Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

机译：大数据驱动作业在离散制造系统中剩余时间预测：基于深入的学习方法

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摘要

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.
机译：实施先进的大数据（BD）分析对于成功融入制造业的人工智能成功。随着智能传感器和事物互联网（物联网）的广泛部署，越来越需要处理制造BD以进行预测制造。在本研究中，我们在基于深度学习（DL）的制造执行期间，构思了在制造执行期间的工作剩余时间（JRT）预测。我们开发了一种JRT预测的过程，包括三个部分：原始数据收集，候选数据集设计和预测建模。首先，历史生产数据由在作业商店中广泛部署的IOT收集。然后，候选数据集正式化以捕获JRT预测的各种贡献因素。此外，构建名为堆叠稀疏自动码器（S-SAE）的DL模型以学习来自高维制造BD的代表特征，以使鲁棒和准确的JRT预测。我们的作品代表了生产过程中JRT预测的第一个DL模型。所提出的方法适用于配备44台机床的大型作业商店，并产生13种零件。最后，实验结果表明，S-SAB模型比以前的线性回归，反向传播网络，多层网络和在JRT预测中的深度信仰网络具有更高的准确性。

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  • 来源
    《International Journal of Production Research》 |2020年第10期|2751-2766|共16页
  • 作者

    Fang Weiguang; Guo Yu; Liao Wenhe; Ramani Karthik; Huang Shaohua;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing Jiangsu Peoples R China|Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA;

    Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing Jiangsu Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing Jiangsu Peoples R China|Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Jiangsu Peoples R China;

    Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA;

    Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing Jiangsu Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    big data; job shop; jobs remaining time prediction; stacked sparse autoencoder; deep learning;

    机译：大数据;求职;乔布斯剩余时间预测;堆积稀疏的autoencoder;深度学习;

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