Cause analysis of the head width narrowing of hot rolled strips based on the kernel principal component analysis
-
摘要: 將核主成分分析方法引入熱軋生產過程的監控與診斷中,根據平方預測誤差統計量進行生產過程監控,然后利用數據重構和優化的鄰域選取策略相結合的方法求出各工藝參數對平方預測誤差統計量的作用,分析引起過程異常的主要工藝參數,最后利用仿真和熱軋帶鋼實際生產數據進行實驗.結果表明:基于核主成分分析的平方預測誤差統計量能較準確診斷過程的異常,并可以找出引起異常的原因,為調整生產過程提供方法支撐,防止次品的出現.Abstract: A method of production quality monitoring and diagnosis based on the kernel principal component analysis was introduced in the hot rolled strip process.The squared prediction error(SPE) statistic was used in process monitoring.The diagnosis criterion could express the influential importance to SPE,which was computed by the data construction method and the optimal neighbor selection strategy.Finally,simulation data and actual production data were used for model validation.The result shows that the SPE statistic based on the kernel principal component analysis can detect the abnormality and track the causes of faults effectively for adjusting the production process to prevent from substandard products.
-
Key words:
- hot rolling /
- strips /
- product quality /
- principal component analysis /
- data analysis
-
點擊查看大圖
計量
- 文章訪問數: 284
- HTML全文瀏覽量: 85
- PDF下載量: 5
- 被引次數: 0
下載:
