Open AccessApplication of artificial neural networks to fault diagnostics of rotor-bearing systems
Author(s) -
Nickolay V. Kornaev,
Елена Корнаева,
Леонид Савин
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/862/3/032112
Subject(s) - rotor (electric) , artificial neural network , bearing (navigation) , vibration , helicopter rotor , fault (geology) , artificial intelligence , computer science , control theory (sociology) , pattern recognition (psychology) , control engineering , engineering , acoustics , mechanical engineering , physics , control (management) , seismology , geology
The article is dedicated to the pattern recognition of unbalanced rotor vibration trajectories. The diagnostics of rotary machines with fluid-film bearings is studied. The feed forward neural networks were used to analyze the measurement data of rotor vibrations and other parameters of the rotor-bearing system. The states of the system were studied at various values of the rotor unbalance. It was shown that the number of training samples and the number of neurons in the input layer have the greatest impact on recognition accuracy. As a result of training the neural network to recognize 3 classes of defects, an accuracy of more than 97% was achieved.