Browsing by Author "Salgado Pilario, Karl Ezra"
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Item Open Access Canonical variate dissimilarity analysis for process incipient fault detection(IEEE, 2018-02-28) Salgado Pilario, Karl Ezra; Cao, YiEarly detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate statistical process monitoring methods are currently established for abrupt fault detection. Among these, canonical variate analysis (CVA) was proven to be effective for dynamic process monitoring. However, the traditional CVA indices may not be sensitive enough for incipient faults. In this work, an extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions. To handle non-Gaussian distributed data, kernel density estimation was used for computing detection limits. A CVA dissimilarity-based index has been demonstrated to outperform traditional CVA indices and other dissimilarity-based indices, namely DISSIM, RDTCSA, and GCCA, in terms of sensitivity when tested on slowly developing multiplicative and additive faults in a CSTR under closed-loop control and varying operating conditions.Item Open Access A kernel design approach to improve kernel subspace identification(IEEE, 2020-05-27) Salgado Pilario, Karl Ezra; Cao, Yi; Shafiee, MahmoodSubspace identification methods, such as canonical variate analysis (CVA), are non-iterative tools suitable for the state-space modelling of multi-input, multi-output (MIMO) processes, e.g. industrial processes, using input-output data. To learn nonlinear system behavior, kernel subspace techniques are commonly used. However, the issue of kernel design must be given more attention because the type of kernel can influence the kind of nonlinearities that the model can capture. In this paper, a new kernel design is proposed for CVA based identification, which is a mixture of a global and local kernel to enhance generalization ability and includes a mechanism to vary the influence of each process variable into the model response. During validation, model hyper-parameters were tuned using random search. The overall method is called Feature-Relevant Mixed Kernel Canonical Variate Analysis (FR-MKCVA). Using an evaporator case study, the trained FR-MKCVA models show a better fit to observed data than those of single-kernel CVA, linear CVA, and neural net models under both interpolation and extrapolation scenarios. This work provides a basis for future exploration of deep and diverse kernel designs for system identification.