Bayesian analysis and prediction of failures in underground trains

Raffaele Argiento, Antonio Pievatolo, Fabrizio Ruggeri

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

11 Citazioni (Scopus)

Abstract

Based upon failure data (date and odometer reading) in the early operating time, a public transportation company is interested in checking the actual reliability of the door opening system of its subway trains before their warranty expires. We consider non‐homogeneous Poisson processes with a double scale because both time and kilometres are recorded for each failure. Different choices to model the relation between operated time and kilometres run are possible: in this paper the kilometres run are incorporated within the intensity function as a random function of time, modelled as a gamma process. Furthermore a periodic component is introduced to deal with the seasonality in the data. Bayesian inference is then carried out via Monte Carlo simulation, obtaining prediction intervals for the expected number of failures during periods of desired length, using only part of the data. The predictions are then compared with the observed data.
Lingua originaleEnglish
pagine (da-a)327-336
Numero di pagine10
RivistaQuality and Reliability Engineering International
Volume19
DOI
Stato di pubblicazionePubblicato - 2003

Keywords

  • Monte Carlo simulation
  • Poisson processes
  • double measurement scale
  • gamma process
  • periodic component

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