Imperfect rail-track inspection scheduling with zero-inflated miss rates

Despite the technological advances in track monitoring, track quality control systems are not always reliable. Inspections may miss defects; all defects may not be registered or recorded due to human or mechanical errors. In this study, first, the authors develop a zero-inflated Bayesian approach to model the rate of missed defects during imperfect inspections where defect arrivals follow a Poisson process. The proposed model reveals information on two parameters: the actual defect arrival rate and the probability of not finding any defects, namely, the zero-inflation rate. Then, the authors study optimizing the track maintenance based on this model. The authors demonstrate that a temporal threshold-type inspection policy is optimal, and they derive this threshold under imperfect inspections. Furthermore, the authors implement a Gibbs sampler for drawing inferences on the posterior distribution of the aforementioned Poisson process parameters from data. Application results provide a realistic perspective on imperfect inspections and offer risk and cost savings in railway systems and, by and large, in other imperfect maintenance systems.

Language

  • English

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Filing Info

  • Accession Number: 01842128
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 11 2022 10:46AM