The article presents an application of a method based on fault tree analysis and the Monte Carlo simulation in the assessment of reliability and availability of the rail means of transport. The primary target of the presented method is a cause and effect assessment of the occurrence of undesirable events, the determination of selected reliability indices and identification of the weakest components of rail vehicle that affect the downtime and technical availability most strongly. To illustrate the application of the presented method, the results of a project involving a 6Dg diesel locomotive, carried out in cooperation with the biggest Polish rail carrier, are shown. The assessment of availability and reliability was based on real operation data of a selected sample of seventy-five locomotives. Based on the collected data from the operation of the 6Dg locomotives, the times-to-failure and the times-to repair models were determined. A fault tree model of the locomotive was developed to assess the influence of the faults of the components on the reliability of the vehicle. A discrete simulation process allows to obtain a chosen characteristics and values of the selected measures, which – according to the authors – may be applied to assess the reliability and availability of the rail vehicles. Specialist software including Weibull++, BlockSim and MiniTab aided calculations were performed. The software includes and advanced solutions in the range of the reliability and availability simulations. The test results indicate that the proposed solution has a wide applicability potential.


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    Title :

    Reliability and availability assessment of diesel locomotive using Fault Tree Analysis


    Contributors:


    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown








    DIESEL LOCOMOTIVE

    SLIVINSKIJ EVGENIJ VASILEVICH / KISELEV VALENTIN IVANOVICH / KOSSOV VALERIJ SEMENOVICH et al. | European Patent Office | 2017

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