Barton, D orcid.org/0000-0003-4986-5817, Wang, S, Zhong, L et al. (5 more authors) (2022) Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 236 (12). pp. 2681-2695. ISSN 0954-4070
Abstract
Based on brake noise dynamometer test data, combined with the artificial intelligent algorithms, frictional braking noise is quantitatively analyzed and predicted in this study. To achieve this goal, a frictional braking noise prediction method is indicatively proposed, which consists of two main parts: first, based on the experimental data obtained from the brake noise dynamometer tests, and combining with the improved Long-Short-Term Memory (LSTM) algorithm, the coefficients of friction (COFs) are predicted under various braking test conditions. Then, based on the predicted braking COFs and other selected critical braking parameters, the quantitative prediction of frictional braking noise is obtained by means of the optimized eXtreme Gradient Boosting (XGBoost) algorithm. Finally, the inherent features of the XGBoost algorithm are employed to qualitatively analyze the importance of the main factors affecting the frictional braking noise. The prediction algorithms of COFs and frictional braking noise are validated by the brake dynamomter test data, and the R2 (R square) scores of both the LSTM and XGBoost prediction algorithms are 0.9, which verifies the feasibility of both algorithms. The main contribution of this work is to predict the braking noise based on a large set of test data and combined with the LSTM and XGBoost artificial intelligent algorithms, which can significantly save time for the brake system development and braking performance testing, and has significance to the rapid prediction of braking frictional noise and fast NVH (noise, vibration, and harshness) optimal design of frictional braking systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IMechE 2021. This is an author produced version of an article published in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Braking noise, friction coefficient, long-short-term memory algorithm, XGBoost model, noise prediction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jan 2022 16:33 |
Last Modified: | 11 Jan 2023 15:25 |
Status: | Published |
Publisher: | SAGE |
Identification Number: | 10.1177/09544070211062276 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181733 |