A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects

In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy...

Smart technology solutions for the NeTIRail-INFRA case study lines: Axle box acceleration and ultra-low cost smartphones

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenance<br/>interventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration...

Rail Condition Monitoring using Axle Box Acceleration Measurements: Defect detection in the Netherlands and Romania

In this paper we discuss rail condition monitoring based on axle box acceleration (ABA) measurements. We present three case studies. The first one in The Netherlands, the detection of local defects with different severity levels (squat A, squat B and squat C) is analysed. The second case from the Faurei testing ring in Romania, the detection of...

A condition-based maintenance methodology for rails in regional railway networks using evolutionary multiobjective optimization: Case study line Braşov to Zărneşti in Romania

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using...