Vibration-based damage detection of rail fastener using fully convolutional networks

Article Engineering, Mechanical

Vibration-based damage detection of rail fastener clip using convolutional neural network: Experiment and simulation

Zhandong Yuan et al.

Summary: A novel and efficient approach using one dimensional CNN for damage detection of railway track structure is proposed and validated through experimental and numerical studies, showing high detection accuracy and robustness.

ENGINEERING FAILURE ANALYSIS (2021)

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Article Engineering, Mechanical

Matrix coupled model for the vehicle-track interaction analysis featured to the railway crossing

Lei Xu et al.

Summary: This paper introduces a matrix coupled model for vehicle-track interaction analysis with a focus on the crossing panel. The model assumes elastic wheel-rail contact and demonstrates the determination of two-point contact during the transition of the wheel load in the crossing panel. Results show the model's capability in capturing the main features of wheel-rail interaction and analyze the impact of track irregularities on the interaction.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

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Article Engineering, Multidisciplinary

Damage detection in a novel deep-learning framework: a robust method for feature extraction

Tian Guo et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

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Article Computer Science, Information Systems

Real-Time Inspection System for Ballast Railway Fasteners Based on Point Cloud Deep Learning

Hao Cui et al.

IEEE ACCESS (2020)

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Article Engineering, Mechanical

Prediction of bolt fastening state using structural vibration signals

Seong-Pil Jeong et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2019)

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Article Construction & Building Technology

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Zhiyi Tang et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2019)

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Article Engineering, Multidisciplinary

Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

Deegan J. Atha et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2018)

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Article Construction & Building Technology

Monitoring bolt tightness of rail joints using axle box acceleration measurements

M. Oregui et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2017)

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Article Acoustics

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Osama Abdeljaber et al.

JOURNAL OF SOUND AND VIBRATION (2017)

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Article Computer Science, Interdisciplinary Applications

Structural Damage Detection with Automatic Feature-Extraction through Deep Learning

Yi-zhou Lin et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2017)

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Article Acoustics

Obtaining railpad properties via dynamic mechanical analysis

M. Oregui et al.

JOURNAL OF SOUND AND VIBRATION (2016)

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Article Engineering, Multidisciplinary

Application of an optimal wavelet transformation for rail-fastening system identification in different preloads

Mohammad Valikhani et al.

MEASUREMENT (2016)

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Article Computer Science, Artificial Intelligence

Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems

Shan Sung Liew et al.

NEUROCOMPUTING (2016)

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Proceedings Paper Acoustics

Multi-task learning and Weighted Cross-entropy for DNN-based Keyword Spotting

Sankaran Panchapagesan et al.

17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES (2016)

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Article Acoustics