Article
Version 2
Preserved in Portico This version is not peer-reviewed
Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder
Version 1
: Received: 15 May 2024 / Approved: 15 May 2024 / Online: 16 May 2024 (08:23:22 CEST)
Version 2 : Received: 16 May 2024 / Approved: 16 May 2024 / Online: 16 May 2024 (12:30:12 CEST)
Version 2 : Received: 16 May 2024 / Approved: 16 May 2024 / Online: 16 May 2024 (12:30:12 CEST)
A peer-reviewed article of this Preprint also exists.
Lu, H.; Zhou, K.; He, L. Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder. Electronics 2024, 13, 2403. Lu, H.; Zhou, K.; He, L. Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder. Electronics 2024, 13, 2403.
Abstract
Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.
Keywords
rolling bearing fault diagnosis; autoencoder; signal denoising; convolution and deconvolution
Subject
Engineering, Mechanical Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment