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李元a,b,c; 周东华c; 谢植b; S.Joe.Qind
a School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
b School of Information Engineering, Chenyang Institute of Chemical Technology, Shenyang 110142, China
c Department of Automation, Tsinghua University, Beijing 100084, China
d Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USA
LI Yuana,b,c; ZHOU Donghuac; XIE Zhib; S.Joe.Qind
a School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
b School of Information Engineering, Chenyang Institute of Chemical Technology, Shenyang 110142, China
c Department of Automation, Tsinghua University, Beijing 100084, China
d Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USA
摘要： Based on principal component analysis, this paper presents an application of faulty sensor
detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To
deal with inconsistency in process data, it is proposed to use the dynamic time warping
technique to make the historical data synchronized first,then build a consistent multi-way
principal component analysis model. Fault detection is carried out based on squared
prediction error statistical control plot. By defining principal component subspace,
residual subspace and sensor validity index, faulty sensor can be reconstructed and
identified along the fault direction. Finally, application results are illustrated in
detail by use of the real data of an industrial PVC making process.
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