<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.</p>
</abstract>

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

School of Merchant Shipping, Shanghai Maritime University

Department of Turbine Engineering, College of Merchant Marine

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN"}

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution...

Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

Pubblicato online: 10 ott 2023

Pagine: 132 - 141

DOI: https://doi.org/10.2478/pomr-2023-0046

Parole chiave
Fault diagnosis, Bearing, PMSM, Bayesian optimisation, CNN

© 2023 Guohua Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

Pubblicato online: 10 ott 2023

Pagine: 132 - 141

DOI: https://doi.org/10.2478/pomr-2023-0046

Parole chiaveFault diagnosis, Bearing, PMSM, Bayesian optimisation, CNN

© 2023 Guohua Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Fault diagnosis, Bearing, PMSM, Bayesian optimisation, CNN