ArticleSensors (Basel, Switzerland)2024
Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network.
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 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Transmission Line Fault Type Identification Based on Polar Lights Optimizer-Selected Features and a Gramian Angular Field Attention Fusion Network.Sensors (Basel, Switzerland) · 2026Article
- An End-to-End Fault Diagnosis Model for Rolling Bearings Based on Multi-Scale Convolution and the Kolmogorov-Arnold Network.Sensors (Basel, Switzerland) · 2026Article
- PaEDNet: A Robust Denoising and Classification Framework for Vibration-Based Fault Diagnosis with Measurement Noise.Sensors (Basel, Switzerland) · 2026Article
- A Physics-Guided Dual-Sensor Framework for Bearing Fault Diagnosis in PMDC Motor Drives.Sensors (Basel, Switzerland) · 2026Article
- Identification of frame geometry and boundary conditions from free-vibration modal signatures.Scientific reports · 2026Article
- Structural damage detection and safety assessment method based on machine vision and machine learning.PloS one · 2026Article
- A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries.Scientific reports · 2025Article
- P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings.Sensors (Basel, Switzerland) · 2025Article
- Transformer fault diagnosis method based on Gramian Angular Field and optimized parallel ShuffleNetV2.Scientific reports · 2025Article
- A mutual mapping model of cross-sections between the axial and normal planes of ball screws based on the forming of helical ball track.Scientific reports · 2025Article
- An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis.Scientific reports · 2025Article
- Fractal and chaotic map-enhanced grey wolf optimization for robust fire detection in deep convolutional neural networks.Scientific reports · 2025Article
- Robust fault detection and classification in power transmission lines via ensemble machine learning models.Scientific reports · 2025Article
- Machine learning based fault classification for improved induction motor performance.PloS one · 2025Article
- Research on Fault Diagnosis of Rolling Bearing Based on Gramian Angular Field and Lightweight Model.Sensors (Basel, Switzerland) · 2024Article
- A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively.
Indexed as
Identifiers
What OpenQuestion holds
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.