A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Multivariate statistical process monitoring

U industrijskoj proizvodnji prisutan je stalni rast zahtjeva, u prvom redu, u pogledu ekonomičnosti proizvodnje, kvalitete proizvoda, stupnja sigurnosti i zaštite okoliša. Put ka ispunjenju ovih zahtjeva vodi kroz uvođenje sve složenijih sustava automatskog upravljanja, što ima za posljedicu mjerenje sve većeg broja procesnih veličina i sve složenije mjerne sustave. Osnova za kvalitetno vođenje procesa je kvalitetno i pouzdano mjerenje procesnih veličina. Kvar na procesnoj opremi može značajno narušiti proizvodni proces, pa čak prouzrokovati ispad proizvodnje što rezultira visokim dodatnim troškovima. U ovom radu se analizira način automatskog otkrivanja kvara i identifikacije mjesta kvara u procesnoj mjernoj opremi, tj. senzorima. U ovom smislu mogu poslužiti različite statističke metode kojima se analiziraju podaci koji pristižu iz mjernog sustava. U radu se PCA i ICA metode koriste za modeliranje odnosa među procesnim veličinama, dok se za otkrivanje nastanka kvara koriste Hotellingova (T**2), I**2 i Q (SPE) statistike jer omogućuju otkrivanje neobičnih varijabilnosti unutar i izvan normalnog radnog područja procesa. Za identifikaciju mjesta (uzroka) kvara koriste se dijagrami doprinosa. Izvedeni algoritmi statističkog nadzora procesa temeljeni na PCA metodi i ICA metodi primijenjeni su na dva procesa različite složenosti te je uspoređena njihova sposobnost otkrivanja kvara.Demands regarding production efficiency, product quality, safety levels and environment protection are continuously increasing in the process industry. The way to accomplish these demands is to introduce ever more complex automatic control systems which require more process variables to be measured and more advanced measurement systems. Quality and reliable measurements of process variables are the basis for the quality process control. Process equipment failures can significantly deteriorate production process and even cause production outage, resulting in high additional costs. This paper analyzes automatic fault detection and identification of process measurement equipment, i.e. sensors. Different statistical methods can be used for this purpose in a way that continuously acquired measurements are analyzed by these methods. In this paper, PCA and ICA methods are used for relationship modelling which exists between process variables while Hotelling\u27s (T**2), I**2 and Q (SPE) statistics are used for fault detection because they provide an indication of unusual variability within and outside normal process workspace. Contribution plots are used for fault identification. The algorithms for the statistical process monitoring based on PCA and ICA methods are derived and applied to the two processes of different complexity. Apart from that, their fault detection ability is mutually compared

An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

Online statistical hypothesis test for leak detection in water distribution networks

This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time.Peer ReviewedPostprint (author's final draft

An Adaptive Nonparametric Modeling Technique for Expanded Condition Monitoring of Processes

New reactor designs and the license extensions of the current reactors has created new condition monitoring challenges. A major challenge is the creation of a data-based model for a reactor that has never been built or operated and has no historical data. This is the motivation behind the creation of a hybrid modeling technique based on first principle models that adapts to include operating reactor data as it becomes available. An Adaptive Non-Parametric Model (ANPM) was developed for adaptive monitoring of small to medium size reactors (SMR) but would be applicable to all designs. Ideally, an adaptive model should have the ability to adapt to new operational conditions while maintaining the ability to differentiate faults from nominal conditions. This has been achieved by focusing on two main abilities. The first ability is to adjust the model to adapt from simulated conditions to actual operating conditions, and the second ability is to adapt to expanded operating conditions. In each case the system will not learn new conditions which represent faulted or degraded operations. The ANPM architecture is used to adapt the model\u27s memory matrix from data from a First Principle Model (FPM) to data from actual system operation. This produces a more accurate model with the capability to adjust to system fluctuations. This newly developed adaptive modeling technique was tested with two pilot applications. The first application was a heat exchanger model that was simulated in both a low and high fidelity method in SIMULINK. The ANPM was applied to the heat exchanger and improved the monitoring performance over a first principle model by increasing the model accuracy from an average MSE of 0.1451 to 0.0028 over the range of operation. The second pilot application was a flow loop built at the University of Tennessee and simulated in SIMULINK. An improvement in monitoring system performance was observed with the accuracy of the model improving from an average MSE of 0.302 to an MSE of 0.013 over the adaptation range of operation. This research focused on the theory, development, and testing of the ANPM and the corresponding elements in the surveillance system