Author:
Kornaev N,Kornaeva E,Savin L
Abstract
Abstract
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.
Reference20 articles.
1. Fault detection analysis in rolling element bearing A review;Gupta;Mater. Today Proceedings,2017
2. FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors;Cabal-Yepez;Mech. Systems and Sig. Proc.,2012
3. A new bearing fault diagnosis method based onmodified convolutional neural networks;Zhang;Chinese Journal of Aeronautics,2020
4. Application of artificial neural networks to diagnostics of fluid-film bearing lubrication;Kornaeva;IOP Conf. Series: Materials Science and Engineering,2019
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