Deep learning-based fault detection in railway wheelsets using time series analysis

Khurram Shaikh; Imtiaz Hussain; Bhawani Shankar Chowdhry

doi:10.22581/muet1982.2303.15

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Deep learning-based fault detection in railway wheelsets using time series analysis

Author(s)

Khurram Shaikh Department of Electronic Engineering, Mehran University of Engineering and Technology,,

Imtiaz Hussain Department of Electrical Engineering, National University of Sciences and Technology,,

Bhawani Shankar Chowdhry Department of Electronic Engineering, Mehran University of Engineering and Technology,,

Abstract

Maintenance of Railway rolling stock is usually scheduled based. However, the mechanical parts, especially the wheelset may wear down prematurely due to several factors such as excessive braking and traction forces and environmental conditions. This makes the scheduled maintenance less effective and sometimes it results in derailments. This paper presents a deep learning-based technique to detect wheel conditions so that maintenance can be performed promptly and efficiently. A time series dataset of axle vibrations is generated using a simulation model of the wheelset. The dataset is then used to train and test the deep learning model. Long short-term memory (LSTM) architecture is selected for this application since it is designed to perform better for time series datasets. The results show good performance in terms of training and testing accuracy. The model is tested in different defect scenarios and the mean square error in the prediction of railway wheelset parameters is around 15%.

Publication Details

Page(s) 154-159

DOI 10.22581/muet1982.2303.15

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 42, Issue: 3, Year: 2023

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Keywords

Railway Wheelset Condition Monitoring Wheel Defects Deep Learning LSTM

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