Adaptive Process Monitoring And Fault Diagnosis Based On Statistical Methods

With the progress of industrial processes in scale and complexity, e?ectivelyprocess monitoring is the key to ensure safety, enhance product quality and economybenefit. It is di?cult to construct the accurate mathematical model for a complexindustrial process. Even if it could be achieved, the predigested equations can onlydescribe partial relationships of energy and mass. These limit the application ofmethods based on the rigorous mathematical model. On the other hand, with therapid development of computer technology, a large amount of process data havebeen collected. How to transform these data into valuable information to improvethe monitoring performance becomes a challenge issue. It is one of the most activeresearch areas in the field of process control.Statistical process monitoring is a method based on statistical theory. Moni-toring model is constructed by analysis and interpretation of the collected data foron-line fault detection and diagnosis so as to reduce the losses caused by faults andenhance product e?ciency.After the content, method and development of statistical process monitoring aregeneralized, the dissertation firstly pays attention to adaptive process monitoring.Under the assumption that the industrial process is stationary or time-invariant,the static principal component analysis (PCA) model used for process monitoringis reasonable. However, the time-varying character of most industrial processesalways violates the assumption. When the PCA model for some particular processcondition is used to monitor these processes with normal changes, a number of falsealarms often arise. Secondly, as a classical linear technique for dimension reductionand pattern classification, Fisher discriminant analysis (FDA) has been approved tooutperform PCA-based or partial least squares-based diagnosis methods. However,the classification performance of FDA will degenerate as long as overlapping samplesexist. Unlike those classification problems in other fields, process fault diagnosis hasa common benchmark class: a normal data set. The improved FDA makes full useof the special class to improve the diagnosis performance of FDA. Finally,"kernel trick"has been used to develop the nonlinear versions of the adaptive monitoringapproach and the improved FDA.Specifically, the main contributions of this dissertation are as follows:1. In view of a time-invariant industrial process, an adaptive process monitoringapproach with variable moving window PCA (VMWPCA) is proposed. On the basisof recursively updating the correlation matrix, the approach combines the movingwindow technique with the classical rank-r singular value decomposition (R-SVD)to construct a new PCA model e?ciently and parsimoniously. Furthermore, thewindow size is an important tunable parameter that varies depending on how fast thenormal process can change. Instead of the fixed moving window selected empirically,a variable moving window strategy and the guidance to select its parameters arediscussed in detail. One merit is that the optimal moving window size is contingentupon the changes of the mean and covariance matrix that represent directly processchanges.2. In view of the nonlinear time-variant industrial process and integrating themerit of kernel PCA (KPCA) to deal with nonlinear data, an nonlinear adaptiveprocess monitoring approach with variable moving window KPCA (VMWKPCA)is proposed. For the kernel version of VMWPCA: VMWKPCA, two key pointsare discussed: e?ciently updating the KPCA model though kernelizing the R-SVDalgorithm and the variable moving window strategy in the feature space.3. An extension of FDA: variable-weighted FDA (VW-FDA) is proposed forprocess fault diagnosis. The approach incorporates the variable weighting into theconventional FDA. The variable weighting finds out each weight vector for all faults.The summed fault data weighted by the corresponding weight vectors involve morefault characteristic information than the original fault data. FDA is performed onthe summed fault data. It is helpful for VW-FDA to extract discriminative fea-tures from overlapping fault data so as to improve the fault diagnosis performanceof FDA. The exact variable weighting is a key procedure to VW-FDA. The vari-able weighting based on the partial F-values with the cumulative percent variation(CPV) is adopted. CPV based on each variable's equivalent variation is proposedto determine candidate variables. Then, the partial F-values can be performed on these candidate variables rather than all variables. It not only reduces the com-putational complexity but also eliminates the redundant variables to improve thevariable weighting performance.4. A new nonlinear fault diagnosis approach with variable-weighted kernel FDA(VW-KFDA) is proposed. The approach incorporates the nonlinear variable weight-ing into KFDA. The nonlinear variable weighting finds out the weighted vector ofeach fault by maximizing the variable weighting criteria: the kernel target alignment(KTA). Unlike the Rayleigh quotient in KFDA, KTA depends on only the kernel ma-trix and doesn't time-consumingly calculate the optimal discriminant vector duringthe optimization procedure. KFDA is performed on the weighted fault data, whichis helpful for VW-KFDA to extract discriminative features from overlapping faultdata so as to improve the fault diagnosis performance of KFDA.