Adaptive fault diagnosis model for high-speed railway turnout using deep convolutional neural networks Online publication date: Wed, 11-Oct-2023
by Xiaoyu Jiang
International Journal of Security and Networks (IJSN), Vol. 18, No. 3, 2023
Abstract: Safety is crucial for high-speed railway transportation. Sensor gadgets monitor train elements to ensure safety and reliability. Accurate fault diagnosis is essential for reliable operation. Manual feature extraction is time-consuming and prone to errors. Intelligent fault diagnostics face challenges in extracting features from railway track images and identifying failures in turnout systems. This paper proposes a deep convolutional neural networks-based adaptive fault diagnosis model (DCNN-AFDM) using the Kaggle Railway Track Fault Detection dataset. DCNN-AFDM incorporates automatic feature extraction, fault type recognition, and comprehensive fault classification. It achieves rapid fault localisation by analysing 2D greyscale images of turnout current signals. The model enhances accuracy and reduces training time. Results show the DCNN-AFDM model has a 96.67% accuracy, 96.11% precision, 98.43% F1-Score, and 95.33% fault detection ratio compared to other approaches.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Security and Networks (IJSN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook