| 基于差分分段PCA的多模态过程故障监测 |
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| 引用本文： | 谭帅,王福利,常玉清,王姝,周贺.基于差分分段PCA的多模态过程故障监测[J].自动化学报,2010,36(11):1626-1636. |
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| 作者姓名： | 谭帅 王福利 常玉清 王姝 周贺 |
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| 作者单位： | 1.东北大学信息科学与工程学院 沈阳 110004 |
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| 摘 要： | 多模态的故障监测是一个复杂的问题, 既需要考虑稳定模态下的故障监测, 也需要考虑不同模态间的过渡故障监测. 不同稳定模态下的数据具有不同的相关关系, 对每个稳定模态需要建立不同的稳定模态模型. 当稳定生产模态发生改变时, 生产过程进入过渡模态, 需要考虑过渡变量相关关系的变化. 本文通过对过渡数据差分, 得到变量相对变化信息. 利用主成分分析(Principal component analysis, PCA)分段对差分变量的相关特性进行分析, 提取相对变化的特征. 最后以实际连续退火机组生产线为背景, 用基于差分分段PCA的多模态方法对多模态过程进行故障监测, 发现算法很好地反映了实际过渡过程机理, 验证了算法的有效性.
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| 关 键 词： | 差分矩阵 分段建模 多模态 故障监测 连续退火机组 |
| 收稿时间： | 2010-1-25 |
| 修稿时间： | 2010-5-13 |
Fault Detection of Multi-mode Process Using Segmented PCA Based on Differential Transform |
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| TAN Shuai,WANG Fu-Li,CHANG Yu-Qing,WANG Shu,ZHOU He.Fault Detection of Multi-mode Process Using Segmented PCA Based on Differential Transform[J].Acta Automatica Sinica,2010,36(11):1626-1636. |
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| Authors: | TAN Shuai WANG Fu-Li CHANG Yu-Qing WANG Shu ZHOU He |
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| Affiliation: | 1.School of Information Science and Engineering, Northeastern University, Shenyang 110004;2.Key Laboratory of Process Industry Automation of Ministry of Education, Northeastern University, Shenyang 110004;3.Drilling and Production Technology Research Institute, Liaohe Oilfield Company, Panjin 124000 |
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| Abstract: | Fault detection for multi-mode process is a complicated problem, as the fault detection for both steady mode and transition mode should be taken into consideration. Different modes are needed for different steady modes because different relations of variables are contained in each mode model. Transition mode is a dynamic process occurring when production changes operating mode. The dynamic characteristic reflects not only the changing variables but also the changing relation of variables. The relative change information can be obtained by differential transform of transition data. Principle components can be extracted by analyzing correlation of differential variables using principal component analysis (PCA). At last, segmented modeling with PCA method is used to monitor multi-mode process of continuous annealing line. The algorithm reflects transition process well and is proved to be efficient. |
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| Keywords: | Difference matrix segmented modeling multi-mode fault detection continuous annealing line |
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