ArticleSensors (Basel, Switzerland)2022
Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A modified transformer based on adaptive frequency enhanced attention, large kernel convolution, and multiscale implementation for bearing fault diagnosis.Scientific reports · 2025Article
- FCRNet: Fast Fourier convolutional residual network for ventilator bearing fault diagnosis.PloS one · 2025Article
- Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.Sensors (Basel, Switzerland) · 2024Article
- Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet.Sensors (Basel, Switzerland) · 2023Article
- A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors.Sensors (Basel, Switzerland) · 2022Review
- Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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