Deep learning for brake squeal: vibration detection, characterization and prediction

• URL: http://arxiv.org/abs/2001.01596v2
• Date: Fri, 15 May 2020 15:57:48 GMT
• Title: Deep learning for brake squeal: vibration detection, characterization and prediction
• Authors: Merten Stender, Merten Tiedemann, David Spieler, Daniel Schoepflin, Norbert Hofffmann, Sebastian Oberst
• Abstract summary: We report on strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. A deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. A recurrent neural network is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system.
• Score: 2.20200533591633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. We report here for the first time on novel strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. Machine learning-based methods to detect and characterize vibrations, to understand sensitivities and to predict brake squeal are applied with the aim to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. The detection method is rooted in recent computer vision techniques for object detection based on convolutional neural networks. It allows to overcome limitations of classical approaches that solely rely on instantaneous spectral properties of the recorded noise. Results indicate superior detection and characterization quality when compared to a state-of-the-art brake squeal detector. In the second part, a recurrent neural network is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. Given a set of multivariate loading conditions, the RNN learns to predict the noise generation of the structure. The validated RNN represents a virtual twin model for the squeal behavior of a specific brake system. It is found that this model can predict the occurrence and the onset of brake squeal with high accuracy and that it can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability.

Related papers

• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults [15.613528945524791]
  This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under varying noise levels. We design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings.
  arXiv  Detail & Related papers  (2024-01-02T12:02:50Z)
• Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery [13.480792901281047]
  This study presents the new benchmark Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF) We draw the focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart.
  arXiv  Detail & Related papers  (2023-12-17T15:27:32Z)
• A Closer Look at Bearing Fault Classification Approaches [1.9531938396288886]
  Rolling bearing fault diagnosis has garnered increased attention in recent years. Recent technological advances have enabled monitoring the health of these assets at scale. Rolling bearing fault diagnosis has garnered increased attention in recent years.
  arXiv  Detail & Related papers  (2023-09-29T06:11:11Z)
• Neuro-symbolic model for cantilever beams damage detection [0.0]
  We propose a neuro-symbolic model for the detection of damages in cantilever beams based on a novel cognitive architecture. The hybrid discriminative model is introduced under the name Logic Convolutional Neural Regressor.
  arXiv  Detail & Related papers  (2023-05-04T13:12:39Z)
• Novel features for the detection of bearing faults in railway vehicles [88.89591720652352]
  We introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults.
  arXiv  Detail & Related papers  (2023-04-14T10:09:50Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning [0.0]
  We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests.
  arXiv  Detail & Related papers  (2022-01-31T18:08:43Z)
• Gradient Starvation: A Learning Proclivity in Neural Networks [97.02382916372594]
  Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks.
  arXiv  Detail & Related papers  (2020-11-18T18:52:08Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.