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Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation.
Paper No: NDE-24-1035
Published Online: November 11, 2024
Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation.
Paper No: NDE-24-1036
Published Online: October 29, 2024
Journal Articles
Zhengchun Qian, Yawen Dong, Chaolin Yang, Wei Zhang, Ruifeng Xu, Zhaoguo Chen, Hongmeng Xu, Huanbo Cheng, Haihong Huang
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. August 2025, 8(3): 031002.
Paper No: NDE-24-1037
Published Online: October 23, 2024
Journal Articles
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. August 2025, 8(3): 031003.
Paper No: NDE-24-1032
Published Online: October 23, 2024
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 1 Flowchart of data preparation and model training in this study More about this image found in Flowchart of data preparation and model training in this study
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 2 Correlation analysis of all the features obtained from one of the data sets (Test_32 kmh_1) More about this image found in Correlation analysis of all the features obtained from one of the data sets...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 3 Distribution of maximum absolute values (SF1) ( a ) before log transformation and ( b ) after log transformation More about this image found in Distribution of maximum absolute values (SF1) ( a ) before log transformati...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 4 Schematic diagram of an LSTM cell More about this image found in Schematic diagram of an LSTM cell
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 5 The architecture of an autoencoder More about this image found in The architecture of an autoencoder
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 6 Flowchart of data in an LSTM autoencoder More about this image found in Flowchart of data in an LSTM autoencoder
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 7 Training and validation loss versus number of epochs plots obtained from the models trained using different data sets: ( a ) Test_16 kmh data set, ( b ) Test_32 kmh_1 data set, ( c ) Test_32 kmh_2 data set, ( d ) Test_32 kmh_3 data set, and ( e ) Test_48 kmh data set More about this image found in Training and validation loss versus number of epochs plots obtained from th...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 8 ( a ) Rail segment with welded joint, and the schematic view of ( b ) the setup of the damage detection system, inspected rail track, and ( c ) the test car equipped with the damage detection system More about this image found in ( a ) Rail segment with welded joint, and the schematic view of ( b ) the s...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 9 Setups of the damage detection system, ( a ) isolators and the holder frame used for the LDV and ( b ) the setup inside the rail car with two controllers and the data acquisition system More about this image found in Setups of the damage detection system, ( a ) isolators and the holder frame...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 10 Anomaly detection results for the LSTM AE model trained using Test_16 kmh data set: ( a ) the MAE loss and ( b ) the detected anomalies in the LDVs time series data More about this image found in Anomaly detection results for the LSTM AE model trained using Test_16 kmh d...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 11 Anomaly detection results for the LSTM AE model trained using Test_32 kmh_1 data set: ( a ) the MAE loss and ( b ) the detected anomalies in the LDVs time series data More about this image found in Anomaly detection results for the LSTM AE model trained using Test_32 kmh_1...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 12 Anomaly detection results for the LSTM AE model trained using Test_32 kmh_2 data set: ( a ) the MAE loss and ( b ) the detected anomalies in the LDVs time series data More about this image found in Anomaly detection results for the LSTM AE model trained using Test_32 kmh_2...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 13 Anomaly detection results for the LSTM AE model trained using Test_32 kmh_3 data set: ( a ) the MAE loss and ( b ) the detected anomalies in the LDVs time series data More about this image found in Anomaly detection results for the LSTM AE model trained using Test_32 kmh_3...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 14 Anomaly detection results for the LSTM AE model trained using Test_48 kmh data set: ( a ) the MAE loss and ( b ) the detected anomalies in the LDVs time series data More about this image found in Anomaly detection results for the LSTM AE model trained using Test_48 kmh d...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 15 ( a ) Schematic view of the 3D FEM model and ( b ) the zoom view of the broken rail segment More about this image found in ( a ) Schematic view of the 3D FEM model and ( b ) the zoom view of the bro...
Image
in Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
> Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Published Online: October 23, 2024
Fig. 16 The discretized motion of the damage detection system at ( a ) the 1st to 23rd time instant, ( b ) the 24th to 29th time instant, ( c ) the 30th to 59th time instant, and ( d ) the 60th to 65th time instant More about this image found in The discretized motion of the damage detection system at ( a ) the 1st to 2...
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