Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks

• URL: http://arxiv.org/abs/2211.09011v1
• Date: Tue, 15 Nov 2022 15:04:06 GMT
• Title: Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks
• Authors: Ant\'ia L\'opez Galdo, Alejandro Guerrero-L\'opez, Pablo M. Olmos, Mar\'ia Jes\'us G\'omez Garc\'ia
• Abstract summary: This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures. The resultant system converts the railway axle vibration signals into time-frequency domain representations, i.e., spectrograms, and, thus, trains a two-dimensional CNN to classify them depending on their cracks.
• Score: 67.60224656603823
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Railway axle maintenance is critical to avoid catastrophic failures. Nowadays, condition monitoring techniques are becoming more prominent in the industry to prevent enormous costs and damage to human lives. This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures applied to time-frequency representations of vibration signals. For this purpose, several preprocessing steps and different types of Deep Learning (DL) and Machine Learning (ML) architectures are discussed to design an accurate classification system. The resultant system converts the railway axle vibration signals into time-frequency domain representations, i.e., spectrograms, and, thus, trains a two-dimensional CNN to classify them depending on their cracks. The results showed that the proposed approach outperforms several alternative methods tested. The CNN architecture has been tested in 3 different wheelset assemblies, achieving AUC scores of 0.93, 0.86, and 0.75 outperforming any other architecture and showing a high level of reliability when classifying 4 different levels of defects.

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