Neuro-Fuzzy Methods for Fault Diagnosis of Nonlinear Systems

A study on neuro-fuzzy systems for fault diagnosis

Fault diagnosis can be facilitated by using either quantitative and qualitative information of the system monitored. This paper presents a novel approach to integrate quantitative and qualitative information in fault-diagnosis, based on the use of neuro-fuzzy systems. In this approach the diagnostic signals residuals are generated and evaluated via a B-Spline functions network. The configuratio...

Fault Diagnosis Using Neuro-fuzzy Systems with Local Recurrent Structure

This paper investigates the development of the Adaptive Neuro-Fuzzy Systems with Local Recurrent Structure (ANFS-LRS) and their application to Fault Detection and Isolation (FDI). Hybrid learning, based on a fuzzy clustering algorithm and a gradientlike method, is used to train the ANFS-LRS. The experimental case study refers to an application of fault diagnosis of an electro-pneumatic actuator...

Neuro-fuzzy methods for nonlinear system identification

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but ...

Fault Diagnosis for Nonlinear Systems

Neuro Fuzzy System for Industrial Processes Fault Diagnosis

In the last decade considerable research efforts have been spent to seek for systematic approaches to Fault Diagnosis (FD) in dynamical systems The problem of fault detection consists in detecting faults in a physical system by monitoring its inputs and outputs .Recently, the research has focused on non-linear systems FDI. Traditionally, the FD problem for non-linear dynamic systems has been ap...