ArticleRoyal Society open science2023
Fault diagnosis for wind turbines with graph neural network model based on one-shot learning.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
- Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Because of the harsh working environment, there is usually a lack of effective data from the gearboxes of wind turbines for fault classification. In this paper, a fault-diagnosis model based on graph neural networks and one-shot learning is proposed to solve the problem of fault classification with limited data. In the proposed method, the short-time Fourier transform is used to convert one-dimensional vibration signals into two-dimensional data, then feature vectors are extracted from the two-dimensional data, and small-sample learning is achieved. An experimental rig was built to simulate the real working scenario of a wind turbine, and the results indicate the high classification accuracy of the proposed method. Furthermore, its effectiveness is verified in comparisons with Siamese, matching and prototypical networks, with the proposed method outperforming all of them.
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Registered trials
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