Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms

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.

Keywords

Braking noise friction coefficient long-short-term memory algorithm XGBoost model noise prediction

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References

North

. A mechanism of disc break squeal. In: 14th FISITA Congress, London, 1972, pp.1–9.

Crolla

Lang

AM.

Brake noise and vibration: the state of the art. Tribol Ser 1991; 18: 165–174.

Yang

Gibson

RF.

Brake vibration and noise: reviews, comments, and proposals. Int J Mater Prod Technol 1997; 12(4-6): 496–513.

Kinkaid

O’Reilly

Papadopoulos

Automotive disc brake squeal. J Sound Vib 2003; 267(1): 105–166.

Oberst

Lai

JC.

Chaos in brake squeal noise. J Sound Vib 2011; 330(5): 955–975.

Ouyang

Nack

Yuan

, et al. Numerical analysis of automotive disc brake squeal: a review. Int J Veh Noise Vib 2005; 1(3-4): 207–231.

North

. Disc brake squeal: a theoretical model. The Motor Industry Research Association, Lindley, Warwickshire, 1972.

Millner

An analysis of disc brake squeal. SAE paper 780332, 1978.

Murakami

Tsunada

Kitamura

A study concerned with a mechanism of disc brake squeal. SAE paper 841233, 1984.

10.

Ahmed

Analysis of disc brake squeal using a ten-degree-of-freedom model. Int J Eng Sci Technol 2011; 3(8): 142–155.

11.

Nishiwaki

Generalized theory of brake noise. Trans Jpn Soc Mech Eng Ser C 1990; 56(527): 1782–1788.

12.

Ouyang

Mottershead

Cartmell

, et al. Friction-induced parametric resonances in discs: effect of a negative friction-velocity relationship. J Sound Vib 1998; 209(2): 251–264.

13.

Butlin

Woodhouse

A systematic experimental study of squeal initiation. J Sound Vib 2011; 330: 5077–5095.

14.

Ostermeyer

Graf

Mode coupling instabilities induced by a periodic coefficient of friction. SAE Int J Passenger Cars Mech Syst 2010; 3(2): 31–37.

15.

Lou

Bai

Disk brake squeal prediction using the ABLE algorithm. J Sound Vib 2004; 272: 731–748.

16.

Gao

Ruan

, et al. Temperature-dependent noise tendency prediction of the disc braking system. Mech Syst Signal Process 2021; 149: 107189.

17.

Fieldhouse

Newcomb

TP.

Double pulsed holography used to investigate noisy brakes. Opt Lasers Eng 1996; 25: 455–494.

18.

Liles

. Analysis of disc brake squeal using finite element methods. In: SAE noise and vibration conference and exposition, 1989. https://doi.org/10.4271/891150.

19.

Lee

Brooks

Barton

, et al. A predictive tool to evaluate disc brake squeal propensity part 1: the model philosophy and the contact problem. Int J Veh Des 2003; 31(3): 289–308.

20.

Lee

Brooks

Barton

, et al. A predictive tool to evaluate disc brake squeal propensity, part 2: system linearisation and modal analysis. Int J Veh Des 2003; 31(3): 309–329.

21.

Ouyang

Mottershead

A moving-load model for disc-brake stability analysis. J Vib Acoust 2003; 125: 53–58.

22.

Denimal

Nacivet

Nechak

, et al. On the influence of multiple contact conditions on brake squeal. Procedia Eng 2017; 199: 3260–3265.

23.

Oberst

Lai

JC.

Statistical analysis of brake squeal noise. J Sound Vib 2011; 330: 2978–2994.

24.

Aleksendrić

Barton

DC.

Neural network prediction of disc brake performance. Tribol Int 2009; 42: 1074–1080.

25.

Xing

, et al. Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle. IEEE Trans Vehicular Technol 2018; 67: 5718–5729.

26.

Xing

Dynamic state estimation for the advanced brake system of electric vehicles by using deep recurrent neural networks. IEEE Trans Ind Electron 2020; 67: 9536–9547.

27.

Stender

Tiedemann

Spieler

, et al. Deep learning for brake squeal. Brake noise detection, characterization and prediction. Mech Syst Signal Process 2021; 149: 107181.

28.

Wang

Guo

Zeng

, et al. Characterization of automotive brake discs with laser-machined surfaces. Automot Innov 2019; 2(3): 190–200.

29.

Hochreiter

Schmidhuber

Long short-term memory. Neural Comput 1997; 9(8): 1735–1780.

30.

Zhang

Guo

Liu

, et al. LSTM-based analysis of industrial IoT equipment. IEEE Access 2018; 6: 23551–23560.

31.

Jiang

Zhang

Xue

, et al. Intelligent deep learning method for forecasting the health evolution trend of aero-engine with dispersion entropy-based multi-scale series aggregation and LSTM neural network. IEEE Access 2020; 8: 34350–34361.

32.

Qiao

Yan

Tang

, et al. Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access 2020; 8: 66257–66269.

33.

Williams

Zipser

. Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Chauvin

Rumelhart

(eds.) Back-propagation: theory, architectures and applications. 1995, pp.433–486. Lawrence Erlbaum Associates Inc., Hillsdale, NJ: Erlbaum.

34.

Gers

Schmidhuber

Cummins

Learning to forget: continual prediction with LSTM. Neural Comput 2000; 12: 2451–2471.

35.

Chen

Guestrin

XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA, 2016, pp.785–794.

36.

Zhang

Qian

Mao

, et al. A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access 2018; 6: 21020–21031.

37.

Chen

Liu

Chen

, et al. XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access 2019; 7: 13149–13158.

38.

Feng

Liu

Shu

A link quality prediction method for wireless sensor networks based on XGBoost. IEEE Access 2019; 7: 155229–155241.

39.

Friedman

JH.

Greedy function approximation: aa gradient bosting machine.. Ann Stat 2001; 29(5): 1189–1232.

40.

Rovine

von Eye

A 14th way to look at a correlation coefficient, correlation as the proportion of matches. Am Stat 1997; 51(1): 42–46.

41.

Reshef

Finucane

, et al. Detecting novel associations in large data sets. Science 2011; 334: 1518–1524.

42.

Kingma

Adam

JBA

. A method for stochastic optimization. San Diego, CA: ICLR, 2015.

43.

Glorot

Bengio

. Understanding of the training deep feed forward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, 2010, vol. 9, pp.249–256.