An impulsive noise filter for rail vibration measurements using a laser Doppler vibrometer on a moving platform

Kaynardag, K.; Yang, C.; Salamone, S.

doi:10.1016/j.ymssp.2024.111918

Mechanical Systems and Signal Processing

2025

DOI: 10.1016/j.ymssp.2024.111918

|View full text |Cite

An impulsive noise filter for rail vibration measurements using a laser Doppler vibrometer on a moving platform

K. Kaynardag,

C. Yang,

S. Salamone

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements

Yang,

Kaynardag,

Lee

et al. 2024

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

View full text Add to dashboard Cite

This study presents an application of Long Short-Term Memory Autoencoder (LSTM AE) for the detection of broken rails based on laser Doppler Vibrometer (LDV) measurements. This work is part of an ongoing project aimed at developing a non-contact damage detection system using LDV measurements. The damage detection system consists of two laser Doppler vibrometers (LDV) mounted on a moving rail car to measure vibrations induced on the rail head. Field tests were carried out at the Transportation Technology Center (TTC) in Pueblo, CO, to collect the vibrational data. This study focused on the detection of broken rails. To simulate the reflected and transmitted waves induced by the broken rail, a welded joint was used. The data was collected from moving LDV measurements, in which the train was operating at three different speeds: 16km/h (10mph), 32km/h (20mph), and 48km/h (30mph). After obtaining the data, filtering and signal processing were applied to obtain the signal features in time and frequency domains. Next, correlation analysis and principal component analysis were carried out for feature selection and dimension reduction to determine the input used to train and test the LSTM AE model. In this study, the LSTM AE models were trained based on different data sets for anomaly detection. Consequently, an automatic anomaly detection approach for anomaly detection based on the LSTM AE model was evaluated. The results show that the LSTM AE model can efficiently detect the anomaly based on the selected features at three different speeds.

show abstract

Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements

Yang,

Kaynardag,

Lee

et al. 2024

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An impulsive noise filter for rail vibration measurements using a laser Doppler vibrometer on a moving platform

Cited by 1 publication

References 64 publications

Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements

Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements

Contact Info

Product

Resources

About