Title :
Fault detection based on modelling electromechanical drive chain
Author :
Füvesi, V. ; Kovács, E.
Author_Institution :
Res. Inst. of Appl. Earth Sci., Univ. of Miskolc, Miskolc, Hungary
Abstract :
A nonlinear model of an electromechanical drive chain used to drive Gamma-log measurement equipment is presented. Locally linear neuro-fuzzy (LLNF) model was developed with LOLIMOT algorithm that is able to capture the dynamic properties of the system over a wide operating range. Some frequently occurring faults were artificially generated and the ability of the fault detection system to capture them was tested. Based on the detected faults, error signal generation was elaborated for end-users using differently structured neural network models. The structures of the used networks were briefly analysed and compared.
Keywords :
electric drives; fault diagnosis; fuzzy set theory; neural nets; power engineering computing; Gamma-log measurement equipment; LLNF model; LOLIMOT algorithm; dynamic properties; electromechanical drive chain modelling; end-users; error signal generation; fault detection; locally linear neuro-fuzzy model; nonlinear model; structured neural network models; Circuit faults; Fault detection; Mathematical model; Sensors; Tracking; Training; Wheels; Electromechanical systems; Fault detection; Neural Networks;
Conference_Titel :
Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2012 International Symposium on
Conference_Location :
Sorrento
Print_ISBN :
978-1-4673-1299-8
DOI :
10.1109/SPEEDAM.2012.6264441
