ArticleSensors (Basel, Switzerland)2022
A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Safe-Domain Generative Adversarial Network with Swin Transformer for Noisy Imbalanced Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- SCBM-Net: a multimodal feature fusion-based dual-channel method for bearing fault diagnosis.Scientific reports · 2025Article
- One-dimensional time-frequency dual-channel visual transformer for bearing fault diagnosis under strong noise and limited data conditions.Scientific reports · 2025Article
- Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments.Scientific reports · 2025Review
- Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers.Sensors (Basel, Switzerland) · 2024Article
- Sample Augmentation Using Enhanced Auxiliary Classifier Generative Adversarial Network by Transformer for Railway Freight Train Wheelset Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2024Article
- Article
- Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions.Sensors (Basel, Switzerland) · 2024Article
- Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features.Sensors (Basel, Switzerland) · 2024Article
- Residual Shrinkage ViT with Discriminative Rebalancing Strategy for Small and Imbalanced Fault Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Cross-Component Transferable Transformer Pipeline Obeying Dynamic Seesaw for Rotating Machinery with Imbalanced Data.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning Method for Rolling Bearing Fault Diagnosis Based on Attention Mechanism and Graham Angle Field.Sensors (Basel, Switzerland) · 2023Article
- Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review.Sensors (Basel, Switzerland) · 2023Review
- A Siamese Vision Transformer for Bearings Fault Diagnosis.Micromachines · 2022Article
- Time Series Forecasting of Motor Bearing Vibration Based on Informer.Sensors (Basel, Switzerland) · 2022Article
- A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In order to improve the diagnosis accuracy and generalization of bearing faults, an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method is proposed in this paper. Firstly, the discrete wavelet transform (DWT) was utilized to decompose the vibration signal into the subsignals in the different frequency bands, and then these different subsignals were transformed into a time-frequency representation (TFR) map by the continuous wavelet transform (CWT) method. Secondly, the TFR maps were input with respective to the multiple individual ViT models for preliminary diagnosis analysis. Finally, the final diagnosis decision was obtained by using the soft voting method to fuse all the preliminary diagnosis results. Through multifaceted diagnosis tests of rolling bearings on different datasets, the diagnosis results demonstrate that the proposed integrated ViT model based on the soft voting method can diagnose the different fault categories and fault severities of bearings accurately, and has a higher diagnostic accuracy and generalization ability by comparison analysis with integrated CNN and individual ViT.
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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.