This paper presents a neural network based fault diagnosing approach which allows
dynamic fault identification. The method utilizes the principal component analysis (PCA) …

A hybrid method of an artificial neural network (ANN) combined with a support vector
machine (SVM) has been developed for the defect diagnostic system applied to the SUAV …

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 …

Safe operation of nuclear power plant is one of the most important tasks in nuclear power
development. This justifies the variety of methods that have been proposed to support the …

Neural networks have been widely used in the field of intelligent information processing
such as classification, clustering, prediction, and recognition. In this paper, a non-parametric …

Gas-path diagnosis is of great efficiency and economic benefit to gas turbines, whose
algorithms are generally developed and tested by simulation. However, the existing …

In the study presented in this paper, the deterioration in the performance of an industrial gas
turbine during the operation design point was simulated by using the thermodynamic …

The problem of fault detection and isolation/identification (FDI) of nonlinear systems using
neural networks is considered in this paper. The proposed FDI approach employs recurrent …

A novel non-linear adaptive fault detection filter (NAFDF) is proposed. It can be used to
detect on-line and isolate the faults of a class of non-linear systems arising from accidental …

In the processing and manufacturing industries, there has been a large push to produce
higher quality products and ensure maximum efficiency of processes, which requires …