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Development of Smart Real-time Fault Detection Approach in Railway Track Deploying a Single Acoustic Emission Sensor Data

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Objective

Railways, integral to global trade and transportation, face infrastructure vulnerabilities from heavy traffic and challenging environments. Timely fault monitoring is pivotal for effective risk mitigation. Acoustic emission (AE) techniques, particularly in non-destructive testing (NDT), provide real-time health monitoring for railway track. However, traditional AE methods, especially signal processing, prove complex and time-intensive for fault localization. Recent studies explore the potential of Artificial Intelligence (AI), specifically neural networks, for fault detection, yet lack a systematic approach to parameter weighting.

Methodology

This study bridges this gap by systematically evaluating weights assigned to the AE parameters to enhance fault detection accuracy. Laboratory and field-tested AE sensor data from railway track assess the significance of parameters such as Amplitude, Peak Frequency, Counts, RMS, Energy, Rise Time, Duration. As carriers of crucial fault information within the AE signal. A novel methodology introduces distinct weights to individual AE parameters based on their importance, refining the AI model's focus on critical attributes. Extensive laboratory experiments simulate damage in various rail sections, assessing the AI model's fault localization efficiency, robustness, and accuracy using an Artificial Neural Network (ANN) model.

Results and Conclusions

The ANN model demonstrates superiority in precise fault localization, affirming its efficacy. The proposed methodology presented herein, validated through rigorous assessments in both controlled laboratory environments and real-world field conditions, stands as a noteworthy advancement in the realm of smart fault detection in real-time.

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Data Availability

Research-related data may be made public upon request without violating the future scope. Researchers can get in touch with the corresponding author for this reason.

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Acknowledgements

The authors would like to thank the Section Engineer, Durgapur, E-RLY, Indian Railway, for supplying the rail section used and for the field data collection for this research. This project has been supported by DST-TSDP, Government of India.

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The author(s) of this article certify that they did not receive any grants, money, or other financial assistance for this article's research, authorship and/or publication.

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  1. Civil Engineering Department, NIT, Durgapur, India

    Apurba Pal & Aloke Kumar Datta

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  1. Apurba Pal
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The experiment was carried out by AP, who also wrote the manuscript. Dr. AKD reviewed the manuscript and supervised the experiment.

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Correspondence to Aloke Kumar Datta.

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Pal, A., Datta, A.K. Development of Smart Real-time Fault Detection Approach in Railway Track Deploying a Single Acoustic Emission Sensor Data. J. Vib. Eng. Technol. (2024). https://doi.org/10.1007/s42417-024-01374-4

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