Paper Info
Title
Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions
Abstract
Rolling bearings are prone to malfunction due to the complexity of rotating equipment and the serious operational environment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has been an effective and efficient tool to ensure reliable and safe operation of rolling bearings. However, environmental noise and variable working conditions affect the data. To accurately detect, locate, and identify bearing faults, we propose a hierarchical branch CNN (HB-CNN) scheme that considers polluted data and variations in the working environment conditions. In our approach, the hierarchical structure of the bearing fault is constructed initially, with three-level labels indicating fault detection, fault isolation, and fault identification. Then, a one-dimensional (1-D) CNN is introduced as a basic building block by stacking small convolutional kernels. Finally, the hierarchical structure is combined with the natural stratification of the network, and the HB-CNN is generated by incorporating the branch structure into the 1-D CNN. To demonstrate the superiority of the HB-CNN, we compare the performance of our HB-CNN scheme with several salient deep learning models on two benchmark datasets. The results demonstrate that the proposed model successfully achieved hierarchical diagnosis of bearing faults and exhibited superior robustness against noise interference and changing working conditions.
Year
DOI
Venue
2021
10.1016/j.knosys.2021.107386
Knowledge-Based Systems
Keywords
DocType
Volume
Convolutional neural network,Hierarchical branch structure,Intelligent bearing diagnosis,Noise interference,Variable working conditions
Journal
230
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kaige Su100.34
Jian-Hua Liu234.04
Hui Xiong332.77