Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 24; no. 1; p. 36
Main Authors: Yan, Xiaoan, Xu, Yadong, She, Daoming, Zhang, Wan
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 24.12.2021
MDPI
Subjects:
Accuracy
Algorithms
Background noise
Bearings
Coders
Deep learning
Fault diagnosis
Feature extraction
Heuristic methods
Machine learning
Mathematical models
Neural networks
Noise
Noise reduction
Optimization
Optimization algorithms
Parameters
Robustness
Roller bearings
rolling bearing
seagull optimization algorithm
stacked variational denoising auto-encoder
Support vector machines
Teaching methods
Vibration analysis
Wavelet transforms
rolling bearing
fault diagnosis
seagull optimization algorithm
stacked variational denoising auto-encoder
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e24010036