Model specific parameters of VAE-IACGAN.

<div><p>Fault detection in high-speed train wheelset bearings is paramount for ensuring operational safety. However, the scarcity of fault samples limits the accuracy of traditional detection methods. To address this challenge, this paper proposes a supervised generative model that integ...

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Bibliographic Details
Main Author:	Jiandong Qiu (20389944) (author)
Other Authors:	Jiaxuan Liu (5185280) (author), Minan Tang (20389941) (author), Dingwang Zhang (22513428) (author), Meng Li (79487) (author)
Published:	2025
Subjects:	Genetics Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified supervised generative model several gan variants satisfying lipschitz constraints prevent gradient vanishing introducing latent variables independent classifier network gradient penalty terms feature levels compared ensuring operational safety 04 %, representing traditional detection methods fault samples limits vae </ p average classification accuracy fault detection samples generated comparative methods variational auto vae ). sample imbalance prior information paper proposes method employs maximum improvement generation process experiments conducted enhance compatibility discriminative capabilities constructed vae acgan framework
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Model specific parameters of VAE-IACGAN.

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