基于支持向量數據描述方法的生產過程監控、診斷與優化

趙啟東; 徐鋼; 黎敏

doi:10.13374/j.issn2095-9389.2016.12.019

留言板

尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

姓名

郵箱

手機號碼

標題

留言內容

驗證碼

基于支持向量數據描述方法的生產過程監控、診斷與優化

doi: 10.13374/j.issn2095-9389.2016.12.019

趙啟東¹,
徐鋼^2,3,
黎敏^3, ,

1. 北京科技大學機械工程學院, 北京 100083;
2. 北京科技大學計算機與通訊學院, 北京 100083;
3. 北京科技大學鋼鐵共性技術協同創新中心, 北京 100083

基金項目:

“十二五”國家科技支撐計劃資助項目(2015BAF30B01)

詳細信息

通訊作者:
黎敏,E-mail:limin@ustb.edu.cn

中圖分類號: TP273
計量
- 文章訪問數: 149
- HTML全文瀏覽量: 31
- PDF下載量: 8
- 被引次數: 0
出版歷程
- 收稿日期: 2016-05-09
- 網絡出版日期: 2021-07-28

Production process monitoring,diagnosis and optimization based on SVDD

ZHAO Qi-dong¹,
XU Gang^2,3,
LI Min^{3
, ,}

1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
3. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China

摘要

摘要: 提出一種基于支持向量數據描述(support vector data description,SVDD)的生產過程監控、診斷與優化方法.首先,利用正常樣本建立SVDD監控模型,獲得控制限;然后,利用貢獻圖對超過控制限的異常點進行診斷,分析引起異常的主要原因;最后,利用鄰近點替換法對異常的生產樣本進行工藝參數優化.將新方法應用于熱軋薄板的生產過程中,結果表明:新方法比傳統的監控方法T^{2 PCA}具有更高的檢出率,且可以實現對異常點的工藝參數優化,使之回到受控狀態.
- 支持向量數據描述 /
- 生產過程 /
- 監控 /
- 診斷 /
- 參數優化
Abstract: A support vector data description (SVDD) was proposed to be introduced in the monitoring, diagnosis and optimization of processes. Firstly, the SVDD monitor model was established to obtain the control limit based on normal samples. Then, the contribution chart was used to diagnose outliners exceeding the control limit in statistics to find the main causes of abnormal production. Finally, the process parameter optimization was performed by the adjacent point replacement. The proposed method was applied to the process of cold rolled sheets. The results show that this method has a higher detection rate than traditional T^{2 PCA} during the production process monitoring, and can optimize the process parameters to make it return to the controlled state.
- support vector data description /
- production processes /
- monitoring /
- diagnosis /
- parameter optimization