Many industrial processes contain both linear and nonlinear parts, and kernel principal
component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the …

The lean blowout is the most critical issue in lean premixed gas turbine combustion.
Decades of research into LBO prediction methods have yielded promising results …

Aiming at the common fault diagnosis problems of rotors in industrial applications. A rotor
system fault diagnosis method based on vibration signal feature vector transfer learning is …

In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical
systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) …

Gas turbines usually operate under complex conditions, such as frequent start-stop, complex
environment (dust, salt fog). There are many sensors equipped in a gas turbine for the sake …

This paper proposes a data‐driven sensor fault detection and isolation approach for the
general class of nonlinear systems. The proposed method uses deep neural network …

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

By sensor accuracy degradation or unwanted alternating current signals, sensor fault with
zero cross point (ZCP) may occur in real systems and conventional data-driven fault …

This study explores the use of the nonlinear principal component analysis (NLPCA)
technique for detecting gas turbine faults. The resurgence of interest in neural network …

The reliability data are very useful in maintenance operations since they are used to
calculate the Mean Time To Failure. However, they are rarely used for the online calculation …