Open AccessBearing Fault Identification Based on Deep Convolution Residual Network
Author(s) -
Tong Zhou,
Yuan Li,
Yijia Jing,
Yifei Tong
Publication year - 2021
Publication title -
mechanika
Language(s) - English
Resource type - Journals
eISSN - 2029-6983
pISSN - 1392-1207
DOI - 10.5755/j02.mech.28265
Subject(s) - residual , convolution (computer science) , bearing (navigation) , deep learning , fault (geology) , artificial intelligence , artificial neural network , feature extraction , computer science , generalization , pattern recognition (psychology) , identification (biology) , convolutional neural network , feature (linguistics) , engineering , algorithm , mathematics , mathematical analysis , linguistics , philosophy , botany , seismology , biology , geology
Bearings are important parts in industrial production and are related to the normal operation of mechanical equipment. For bearing fault identification traditional method often includes feature extraction, which involves professional prior knowledge and is time-consuming. This paper proposes the deep convolution residual network (DCRN) to identify the bearing fault. Based on the end-to-end learning characteristics of deep neural networks, this method can directly use raw data for training, and does not require feature extraction. Moreover, under the effect of skip connection, DCRN can exert the powerful fitting ability of deep neural network. In this paper, by stacking residual blocks, three different architecture of DCRN are designed and all three achieve very high accuracy, respectively 99.60%, 99.71% and 99.81%. Compared with other methods, DCRN have better generalization performance.