Fault detection is a crucial step in condition based maintenance requiring. The importance of fault diagnosis necessitates an efficient and effective failure pattern identification method. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) emerging as prospective pattern recognition techniques in fault diagnosis have been showing its adaptability, flexibility and efficiency. Reg...

in this study, detection and identification of common faults in industrial gas turbines is investigated. we propose a model-based robust fault detection(fd) method based on multiple models. for residual generation a bank of local linear neuro-fuzzy (llnf) models is used. moreover, in fault detection step, a passive approach based on adaptive threshold is employed. to achieve this purpose, the a...

Prompt detection and diagnosis of process malfunctions are strategically important due to economic and environmental demands required for industries to remain competitive in world markets. In this paper a new formulation of the computation of the disturbance and fault distribution matrices is suggested for Neuro-Fuzzy and De-coupling Fault Diagnosis Scheme (NFDFDS). NFDFDS is a multiple-model f...

The main goal of a fault detection and diagnosis of manufacturing systems is to prevent an undesired stop of the production line and its corresponding cost. Modern manufacturing systems are a very complex domain due to all the physical variables involved and their correlations. This represent a very challenging task for monitoring systems. The present article shows a fault detection and diagnos...

The detection and fault identification of the partial discharge is the important aspect for the monitoring of the power equipment. For the lacks of the traditional detection methods, this paper analyzed the UHF signal characteristic parameters of the power equipment’s partial discharge, and developed the UHF detection system of the partial discharge. The A/D technology of the gain equalization ...