ArticleSensors (Basel, Switzerland)2023
Intelligent Compound Fault Diagnosis of Roller Bearings Based on Deep Graph Convolutional Network.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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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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.
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Who cites it
3 citing papers in PubMed.
- A Method for the Intelligent Identification of the Operating Conditions of Deep-Sea Submersibles and Its Application.Sensors (Basel, Switzerland) · 2025Article
- Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial-Temporal Features Fusion.Sensors (Basel, Switzerland) · 2025Article
- Elevator fault diagnosis based on digital twin and PINNs-e-RGCN.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings.
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Registered trials
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