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

<p>Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties. Therefore, the prediction of the remaining life of bearings is of great significance. The purpose of this paper is to analyze the approximate modeling technology and develop a framework for combined approximate modeling technology. A multi-strategy radial-based approximate model optimization model is proposed based on the limitations of radial-based approximate model technology. Utilizing the weight coefficient solving technique, the variable confidence RBF model, i.e., RBF-LSTM model, is established. Propose the remaining methods for life prediction using the bearing life prediction process. The RBF-LSTM combined approximation model is used to construct the evaluation index for rolling bearing remaining life prediction. Using the empirical analysis method, the optimization effects of different models and the accuracy of bearing remaining life prediction are analyzed, respectively. Experiments show that the data range of the RBF-LSTM combined approximation model is between [23,52], the overall fluctuation range of the data is not large, and the time taken is only 31 s. After 230 calculations, the model optimization effect is better. In the remaining life validation, the starting values of 132h and 148h are less different from real life, only 1.53h and 1.3h, respectively, and the model prediction accuracy is high.</p>
</abstract>

Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties. Therefore, the prediction of the remaining life of bearings is of great significance. The purpose of this paper is to analyze the approximate modeling technology and develop a framework for combined approximate modeling technology. A multi-strategy radial-based approximate model optimization model is proposed based on the limitations of radial-based approximate model technology. Utilizing the weight coefficient solving technique, the variable confidence RBF model, i.e., RBF-LSTM model, is established. Propose the remaining methods for life prediction using the bearing life prediction process. The RBF-LSTM combined approximation model is used to construct the evaluation index for rolling bearing remaining life prediction. Using the empirical analysis method, the optimization effects of different models and the accuracy of bearing remaining life prediction are analyzed, respectively. Experiments show that the data range of the RBF-LSTM combined approximation model is between [23,52], the overall fluctuation range of the data is not large, and the time taken is only 31 s. After 230 calculations, the model optimization effect is better. In the remaining life validation, the starting values of 132h and 148h are less different from real life, only 1.53h and 1.3h, respectively, and the model prediction accuracy is high.

Residual life prediction of bearings based on RBF approximation models

Department of Science and Technology, Hubei Industry Polytechnic

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Residual life prediction of bearings based on RBF approximation models"}

Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties....

Residual life prediction of bearings based on RBF approximation models

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 10 mar 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01329

Słowa kluczowe
RBF approximation model, Weight coefficient solution, RBF-LSTM combination, Bearing remaining life, Remaining life prediction

© 2023 Qiang Zhen et al., published by Sciendo

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

Residual life prediction of bearings based on RBF approximation models

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 10 mar 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01329

Słowa kluczoweRBF approximation model, Weight coefficient solution, RBF-LSTM combination, Bearing remaining life, Remaining life prediction

© 2023 Qiang Zhen et al., published by Sciendo

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

Słowa kluczowe
RBF approximation model, Weight coefficient solution, RBF-LSTM combination, Bearing remaining life, Remaining life prediction