ArticleSensors (Basel, Switzerland)2019
Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Edge Intelligence-Driven Bearing Fault Diagnosis: A Lightweight Anti-Noise Diagnostic Framework.Sensors (Basel, Switzerland) · 2026Article
- Disentangle-and-aggregate feature learning (DAFNet) for motor bearing fault diagnosis.Scientific reports · 2026Article
- Article
- Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.Sensors (Basel, Switzerland) · 2024Article
- Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox.Sensors (Basel, Switzerland) · 2023Article
- An AVMD-DBN-ELM Model for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion.Bioengineering (Basel, Switzerland) · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- Data-Driven Fault Diagnosis for Electric Drives: A Review.Sensors (Basel, Switzerland) · 2021Review
- Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression.Entropy (Basel, Switzerland) · 2021Article
- Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The rolling bearing is an important part of the train's running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.