Deep learning for brake squeal: Brake noise detection, characterization and prediction

Stender, Merten; Tiedemann, Merten; Spieler, David; Schoepflin, Daniel; Hoffmann, Norbert; Oberst, Sebastian

doi:10.1016/j.ymssp.2020.107181

Cited by 64 publications

(20 citation statements)

References 86 publications

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“…Stender et al [97] approaches the acoustic brake squeal problem in two steps: brake NVH assessment and brake squeal prediction. For the NVH assessment, a Short-time Fourier transform creates 2D data representations, which pass through random modification to augment the data and avoid overfitting before training a CNN.…”

Section: Prognosis and Mission Planningmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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Section: Prognosis and Mission Planningmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Deep learning (DL) is a new trend among researchers in the domain of condition monitoring of machinery due to its 2064 Structural Health Monitoring 21 (5) promising result and automated feature learning. [9][10][11][12][13][14][15][16] An extensive review of using deep learning for machine health monitoring is presented in Ref. 10.…”

Section: Introductionmentioning

confidence: 99%

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

Yaghoubi

Cheng

Paepegem

et al. 2022

Structural Health Monitoring

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Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster–Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.

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“…With the rapid development of machine learning theories, scholars have tried to adopt machine learning methods to solve the problems in industrial applications, and these methods have been successfully used in many different areas, including in prediction of disk brake performance, 24 braking intensity classification and quantitative recognition in various deceleration scenarios, 25 electric vehicle brake pressure probability estimation, 26 and brake noise characterization and detection. 27 To save the testing time and efforts for the fast development of braking systems and their components, in the present work, machine learning methods are employed to quantitatively predict the COFs and braking noise based on the experimental data under various testing conditions, and qualitatively analyze the main effect factors on braking noise. The main contents of this article can be summarized as the followings:…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhong

Niu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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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.

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Deep learning for brake squeal: Brake noise detection, characterization and prediction

Cited by 64 publications

References 86 publications

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

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

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