An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

Abstract Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis, while the noise mixed in measured signals harms the prediction accuracy of networks. Existing denoising methods in neural networks, such as using complex network architectures and intr...

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Bibliographic Details
Main Authors: Baoxuan Zhao, Changming Cheng, Guowei Tu, Zhike Peng, Qingbo He, Guang Meng
Format: Article
Language:English
Published: SpringerOpen 2021-05-01
Series:Chinese Journal of Mechanical Engineering
Subjects:
Machine fault diagnosis
Reproducing kernel Hilbert space (RKHS)
Regularization problem
Denoising layer
Neural network
Online Access:https://doi.org/10.1186/s10033-021-00564-5

Internet

https://doi.org/10.1186/s10033-021-00564-5

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