PROCESS FAULT DETECTION AND DIAGNOSIS BASED ON PRINCIPAL COMPONENT ANALYSIS
Conventional process fault detection and diagnosis technique need an in-depth comprehension and mastery in process mechanism models, which have to obtain very particular process transcendental knowledge and various physical and chemical parameters. It is very time-consuming and difficult for actual production processes. A novel process fault detection and diagnosis technique based on principal component analysis (PCA) is presented and discussed. The proposed method reduces the dimensionality of the original data set by the projection of the data set onto a smaller subspace defined by the principal components through PCA, and the multivariate statistical process control charts, for example, HotellingT2, Q and contribution charts are used to detect and diagnose the process faults. The monitoring performance of the proposed method to a typical continuous production process indicates that the fault diagnosis model constructed by PCA can efficiently be used to extract the main variable information of original data set independent of the process mechanism, and detect the abnormal change of the process.
Principal component analysis Condition monitoring Fault detection
TAO HE WEI-RONG XIE QING-HUA WU TIE-LIN SHI
Sch.of Mech.Engin., Hubei Univ.of Tech., Wuhan 430068, China;Hubei Key Lab of Modern Manufacture Qua Sch.of Mech.Engin., Hubei Univ.of Tech., Wuhan 430068, China;Hubei Key Lab of Modern Manufacture Qua
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3551-3556
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）