We have recently developed a novel Dynamic Weight In Motion (DWIM) system for freight carriages, which is based on a combination of Discrete Fourier Transform (DFT) and ELastic NET (ELNET) linear regression. To improve this method, we propose an unsupervised domain adaptation method based on subspace alignment. This learns a mapping function to align the features extracted from data of carriages of known load in a given DWIM setting with those extracted from carriages of unknown load in another positions. The application of the proposed method provides promising results.

Unsupervised domain adaptation for dynamic in weighing motion system of freight rail carriages under varying ballast conditions

Compare M.;Zio E.
2020-01-01

Abstract

We have recently developed a novel Dynamic Weight In Motion (DWIM) system for freight carriages, which is based on a combination of Discrete Fourier Transform (DFT) and ELastic NET (ELNET) linear regression. To improve this method, we propose an unsupervised domain adaptation method based on subspace alignment. This learns a mapping function to align the features extracted from data of carriages of known load in a given DWIM setting with those extracted from carriages of unknown load in another positions. The application of the proposed method provides promising results.
2020
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
978-981-14-8593-0
Domain adaptation, subspace alignment
Dynamic Weight In Motion
Railway industry
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181245
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