Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.
Sensors (Basel)
; 22(5)2022 Feb 22.
Article
in English
| MEDLINE | ID: covidwho-1742604
ABSTRACT
The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Neural Networks, Computer
Type of study:
Experimental Studies
Limits:
Humans
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
S22051705
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