Title :
Neural network monitoring system used for the frequency vibration prediction in gas turbine
Author :
Ben Rahmoune, Mohamed ; Hafaifa, Ahmed ; Guemana, Mouloud
Author_Institution :
Appl. Autom. & Ind. Diagnostics Lab., Univ. of Djelfa, Djelfa, Algeria
Abstract :
Rotating machines are widely used in the industry; all these machines in operation produce vibrations phenomena caused by dynamic forces generated in moving parts of these equipments. This work propose the development of fault diagnosis system for the vibration detection and isolation based on artificial intelligence using artificial neural networks, applied to a gas turbine system, in order to secure the vibration frequency acquired by the sensors at bearings and then the prediction of the behavior of the turbine shaft. The obtained results are satisfactory and given the justification for the use of artificial neural networks for diagnosis of rotating machinery.
Keywords :
artificial intelligence; electric machines; fault diagnosis; gas turbines; mechanical engineering computing; monitoring; neural nets; vibrations; artificial intelligence; artificial neural networks; fault diagnosis system; frequency vibration prediction; gas turbine; neural network monitoring system; rotating machines; vibration detection; vibration isolation; Artificial neural networks; Biological neural networks; Mathematical model; Shafts; Turbines; Vibrations; Diagnosis; defects; faults detection; faults isolation; gas turbine; generation of residues; modeling; neural networks; vibration;
Conference_Titel :
Control, Engineering & Information Technology (CEIT), 2015 3rd International Conference on
Conference_Location :
Tlemcen
DOI :
10.1109/CEIT.2015.7233185