Abstract
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.
Highlights
Process monitoring refers to various methods used for the detection, diagnosis, and prognosis of faults in industrial plants [1,2]
Having motivated the importance of kernel methods in the previous section, the rest of the paper is dedicated to a review of their applications to process monitoring
The winner of the Netflix Prize for a video recommender system was an ensemble of more than 100 learners [40], the winner of the Higgs Boson machine learning challenge was an ensemble of 70 deep neural networks that differ in initialization and training data sets [328], and it was reported that 17 out of the 29 challenges published in a machine learning competition site called Kaggle in 2015 alone were won by an ensemble learner called XGBoost [329]
Summary
Process monitoring refers to various methods used for the detection, diagnosis, and prognosis of faults in industrial plants [1,2]. Controls are already in place to compensate for process upsets and disturbances, process faults can still occur [1]. These faults include sensor faults (e.g., measurement bias), actuator faults (e.g., valve stiction), fouling, loss of material, drifting reaction kinetics, pipe blockages, etc. Diagnosis, and prognosis methods aim to, respectively, determine the presence, identify the cause, and predict the future behavior of these process anomalies [2,4]. Process monitoring is a key layer of safety for maintaining an efficient and reliable operation of industrial plants [5]
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