基于图像随机分布模型的电熔镁炉工况识别
作者:
作者单位:

东北大学

作者简介:

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Conditions recognition of fused magnesia furnace based on dynamic characteristics of B-spline network
Author:
Affiliation:

Northeastern University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    电熔镁炉制备电熔镁砂的工艺过程中,会交替出现正常熔炼、加料和欠烧等多种不同工况,其中,欠烧工况分辨难度最大且最关键,及时针对欠烧采取相应的处理以保证生产过程的正常进行.目前,欠烧工况的识别主要依靠人工经验完成,这种方式的准确性取决于人的经验水平和生理状态,且工人劳动强度大、存在容易漏检误检的问题.本文依据不同工况下炉口火焰图像中具有的动态特征,提出一种基于B样条(B-Spline)动态网络动态特性的工况识别技术.首先,建立炉口火焰的线性动态系统模型来刻画系统的动态特性,然后,设计基于子空间主要角度的核函数来度量火焰动态模型相似度.对比实验表明本文设计的基于B-Spline动态网络动态特性的工况识别技术具有更好的分类精度和更高的效率.

    Abstract:

    In the process of preparing fused magnesia in fused magnesium furnace, different working conditions such as Smelting condition, Feeding condition and Semi-fused condition alternately occur.Among them, Semi-fused condition is the most difficult and critical to distinguish, and corresponding treatment should be taken for Semi-fused condition in time to ensure the normal production process.At present, the identification of Semi-fused conditions mainly depends on manual experience. The accuracy of this method depends on the experience level and physiological state of workers,in addition, the labor intensity of the workers is high and it is easy to miss detection and misdetect. Based on the dynamic characteristics of the furnace flame image under different working conditions, this paper proposes a working condition recognition technology based on the dynamic characteristics of the B-spline dynamic network. First of all, establish the linear dynamic system model of the furnace flame to describe the dynamic characteristics of the system. Then, the kernel function based on subspace principal angles is designed to measure the similarity of the flame dynamic models. The comparison experiment shows that the design of the working condition recognition technology based on the dynamic characteristics of the B-Spline dynamic network has the better classification accuracy and higher efficiency.

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  • 收稿日期:2020-05-14
  • 最后修改日期:2020-09-16
  • 录用日期:2020-09-25
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