Open AccessResearch on the Method of Rotary Machinery Fault Diagnosis based on PCA and DBN
Author(s) -
H P Li,
Zh L Qi,
Junlin Hu,
X Y Zhang
Publication year - 2021
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/1043/2/022044
Subject(s) - deep belief network , fault (geology) , principal component analysis , artificial intelligence , pattern recognition (psychology) , feature extraction , computer science , signal (programming language) , feature (linguistics) , vibration , transmission (telecommunications) , path (computing) , engineering , deep learning , telecommunications , linguistics , philosophy , physics , quantum mechanics , seismology , programming language , geology
Aiming at the difficulty of complex vibration signal transmission path, great influence of different sensor positions on diagnosis results and difficulty in feature extraction of rotary machinery fault diagnosis, a new fault diagnosis method based on Principal Component Analysis (PCA) and Deep Belief Network (DBN) is proposed. The framework of the method is constructed. The theory of PCA and DBN are introduced. And the validity and superiority of the proposed method are verified by the experimental data of typical rotary machinery.