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Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

机译：无缝管壳体轧制生产过程监控与故障诊断

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摘要

Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics. It is difficult to build its mechanism model. In this paper we divide production data into several subperiods by K-means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method. Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision.
机译：无缝管的连续轧制过程具有许多特点，包括多周期，强非线性，动态特性快速变化。建立其机制模型很困难。本文通过结合生产过程的K-均值聚类算法将生产数据划分为几个子阶段。然后建立基于多阶段MPCA方法的连续轧制生产监控与故障诊断模型。仿真实验表明，基于多阶段MPCA方法的轧钢生产过程监控与故障诊断模型是有效的，具有良好的实时性，高可靠性和精度。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第21期|219710.1-219710.12|共12页
  • 作者

    Xiao Dong; Gao Xuyang; Wang Jichun; Mao Yachun;

  • 作者单位

    Northeast Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China|Northeastern Univ, Informat Sci & Engn Sch, Shenyang 110004, Peoples R China;

    Northeast Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China|Northeastern Univ, Informat Sci & Engn Sch, Shenyang 110004, Peoples R China;

    Liaoning Ind Univ, Coll Sci, Jinzhou 121000, Peoples R China;

    Northeastern Univ, Coll Resources & Civil Engn, Shenyang 110004, Peoples R China;

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