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Dec 13, 2022 · TL is a new fault diagnosis method that maps data from two different kinds of distributions into a common space, minimizing the distance between ...
Dec 1, 2022 · The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN ...
A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute ...
Most transfer learning (TL) models generally need the fault data from similar scenarios to achieve cross-domain bearing fault diagnosis.
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Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis. Sensors 2022, 22, 9759. https://doi.org/10.3390/s22249759. AMA ...
Fault diagnosis results of compound working condition. Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis. Article.
Discussion. This study proposes a digital twin-assisted dual transfer (DTa-DT) method with information and model adaptation for rolling bearing fault diagnosis.
In this study, a deep transfer learning method for bearing fault diagnosis, domain separation reconstruction adversarial networks (DSRAN), was proposed.
A rolling bearing fault diagnosis method suitable for different working conditions based on simulating the real industrial scene.
Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis · Engineering. Italian National Conference on Sensors · 2022.