This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA)
for handling nonlinear dynamic systems. The proposed method is entitled Moving Window …

Vibration analysis is widely used as an efficient condition monitoring (CM) tool for rotating
machines in various industries. Fault detection and diagnosis (FDD) models play an …

Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the
hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault …

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 proposes a new method for fault detection using a reduced kernel principal
component analysis (RKPCA). The proposed RKPCA method consists on approximating the …

An improved method for process monitoring and fault detection called dynamic
reconstruction principal component analysis (DRPCA) is proposed. By extracting direct …

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 …

This paper proposes a new reduced kernel method for monitoring nonlinear dynamic
systems on reproducing kernel Hilbert space (RKHS). Here, the proposed method is a …

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

This paper suggests an extension of the PCMD index proposed for sensor fault diagnosis in
linear systems to the nonlinear case. The proposed index is entitled KPCMD, and it is based …