Pareto-based maintenance decisions for regional railways with uncertain weld conditions using the Hilbert spectrum of axle box acceleration

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Abstract

This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multi-objective optimization is employed to optimize the objective functions so that the trade-offs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania, Romania are employed.

Original languageEnglish
Number of pages12
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusE-pub ahead of print - 14 Jun 2018

Keywords

  • Acceleration
  • Acceleration measurements
  • Axles
  • Degradation
  • Evolutionary multi-objective optimization
  • Maintenance
  • Maintenance engineering
  • Rail transportation
  • Rails
  • Railway infrastructure
  • Welding

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