Detection and Classification System for Rail Surface Defects Based on Eddy Current.

Tiago A. Alvarenga,Augusto S. Cerqueira,Alexandre L. Carvalho,Luciano M. A. Filho,Leonardo M. Honorio,Rafael A. Nobrega

doi:10.3390/s21237937

Abstract

The prospect of growth of a railway system impacts both the network size and its occupation. Due to the overloaded infrastructure, it is necessary to increase reliability by adopting fast maintenance services to reach economic and security conditions. In this context, one major problem is the excessive friction caused by the wheels. This contingency may cause ruptures with severe consequences. While eddy’s current approaches are adequate to detect superficial damages in metal structures, there are still open challenges concerning automatic identification of rail defects. Herein, we propose an embedded system for online detection and location of rails defects based on eddy current. Moreover, we propose a new method to interpret eddy current signals by analyzing their wavelet transforms through a convolutional neural network. With this approach, the embedded system locates and classifies different types of anomalies, enabling an optimization of the railway maintenance plan. Field tests were performed, in which the rail anomalies were grouped in three classes: squids, weld and joints. The results showed a classification efficiency of ~98%, surpassing the most commonly used methods found in the literature.

Highlights

The railway is an essential and advantageous means of transport well consolidated in several countries
With the development of research and new tools to assist in the railway system’s logistics and operation stages, the corrective maintenance steps have been replacing by preventive maintenance steps
In [13], the author used signals from accelerometers installed on the train to capture irregularities in the tracks, in this case, 34 squat samples were collected and later analyzed using the wavelet transform

Summary

Introduction

The railway is an essential and advantageous means of transport well consolidated in several countries. The degree of reliability in cargo transportation is ever-increasing due to personal safety, preventing loss of assets, meeting deadlines and reducing maintenance expenses. These factors have a direct impact on the market competitiveness. The expansion of the number of loads and the increase of the rails’ use causes acceleration of the rails’ wear. This results in increased surface defects on the rails, altering the balance of contact between the rail and the wheels and cause accidents and even derailment [1]. With the development of research and new tools to assist in the railway system’s logistics and operation stages, the corrective maintenance steps have been replacing by preventive maintenance steps

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