Authors:
Krzysztof Patan
;
Józef Korbicz
and
Gracjan Głowacki
Affiliation:
Institute of Control and Computation Engineering, University of Zielona Góra, Poland
Keyword(s):
Neural networks, DC motor, modelling, density shaping, fault detection, fault isolation, fault identification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Soft Computing
Abstract:
The paper deals with a model-based fault diagnosis for a DC motor realized using artificial neural networks. The considered process was modelled by using a neural network composed of dynamic neuron models. Decision making about possible faults was performed using statistical analysis of a residual. A neural network was applied to density shaping of a residual, and after that, assuming a significance level, a threshold was calculated. Moreover, to isolate faults a neural classifier was developed. The proposed approach was tested in DC motor laboratory system at the nominal operating conditions as well as in the case of faults.