ArticleSensors (Basel, Switzerland)2024
A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- ACSE-RNformer: Amplitude-Calibrated Sequence Embedding and Response-Normalized Transformer for Vibration-Based Rotating Machinery Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- A CNN Feature Extraction and BKA-Optimized LSSVM Classification Method for Small-Sample Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- MGFR-ViT: A Multi-Scale Gated Feature Refinement Vision Transformer for Vibration-Based Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Let the Model Choose Its Own Frequency: An Adaptive Frequency-Aware Inverted Transformer for Noise-Robust Gearbox Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Transmission Line Fault Type Identification Based on Polar Lights Optimizer-Selected Features and a Gramian Angular Field Attention Fusion Network.Sensors (Basel, Switzerland) · 2026Article
- A Two-Stage Interpretable Fault Diagnosis Approach for Bearings Based on EBM.Sensors (Basel, Switzerland) · 2026Article
- An optimized ensemble framework for machinery fault detection in IoT environments.Scientific reports · 2026Article
- Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices.
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