Open AccessA neural network based method for sensitive frequency component analysis of cavitation fault
Author(s) -
Jinxin Yu,
Dongliang Fu,
Pu Zhang,
Jiatong Li,
Fei Ye,
Yufei Shen
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/552/1/012012
Subject(s) - artificial neural network , hilbert–huang transform , signal (programming language) , fault (geology) , cavitation , sensitivity (control systems) , computer science , pattern recognition (psychology) , feature (linguistics) , vibration , artificial intelligence , biological system , component (thermodynamics) , acoustics , engineering , electronic engineering , physics , computer vision , geology , linguistics , philosophy , filter (signal processing) , seismology , biology , programming language , thermodynamics
In General, the frequency feature of cavitation was obtained by comparing it with normal signal. However, when there were a large number of samples, it was difficult to analyse the sensitive features of cavitation fault effectively. So, a neural network based method for sensitive frequency component analysis of cavitation fault was proposed, and Empirical Mode Decomposition(EMD) method, Fourier Transform and neural network were used. Firstly, the raw vibration signal was decomposed to 5 Intrinsic Mode Function(IMF) components and the frequency spectrum of each component were computed. So, the dataset of raw signal was divided into 5 datasets which contained different frequency components. And a neural network was built, trained and tested by the different datasets. By comparing the diagnosis accuracy of the neural network, the sensitivity of different IMF was analysed. And it is verified that the method can effectively analyse the sensitive frequency components of cavitation faults, reduce the size of the neural network.