ArticleScientific reports2024
Bearing fault detection by using graph autoencoder and ensemble learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Fault Detection in Axial Deformation Sensors for Hydraulic Turbine Head-Cover Fastening Bolts Using Analytical Redundancy.Sensors (Basel, Switzerland) · 2026Article
- A domain generalization network for imbalanced machinery fault diagnosis.Scientific reports · 2024Article
- A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults.Scientific reports · 2024Article
- A novel design of journal bearings for stability under shock loads.Scientific reports · 2024Article
- An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.
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Registered trials
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