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 …

In recent years, fault detection and diagnosis for industrial processes have been rapidly
developed to minimize costs and maximize efficiency by taking advantages of cheap …

This paper proposes a new machine learning method for fault detection using a reduced
kernel partial least squares (RKPLS), in static and online forms, for handling nonlinear …

This paper deals with the fault detection of a pneumatic control valve using canonical variate
analysis (CVA). CVA can find the optimal linear combinations of p-window and f-window …

In the past twenty-five years, structural health monitoring (SHM) has become an increasingly
significant topic of investigation in the civil and structural engineering research community …

Data-driven techniques have been receiving considerable attention in the industrial process
monitoring field due to their major advantages of easy implementation and less requirement …

Abstract Kernel Principal Components Analysis (KPCA) method it is the frequently used
among the other kernel methods due to their easiness and it competence in modeling …

Abstract Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness
that cannot be cured, but the early diagnosis allows precautionary measures to be taken …

The paper focuses on the importance of prompt and efficient process fault detection in
contemporary manufacturing industries, where product quality and safety protocols are …

Abstract Kernel Principal Component Analysis (KPCA) is an efficient multivariate statistical
technique used for nonlinear process monitoring. Nevertheless, the conventional KPCA …