CERN has the legal obligation to protect the public and the people working on its site from any unjustified exposure to ionizing radiation. Therefore, several monitoring systems are operated at CERN to evaluate the radiological impact of CERN’s accelerators and installations by active monitoring. Having highly reliable and available monitoring systems is hence a crucial factor to ensure a safe operation and steady availability of the accelerator. Besides designing reliable systems or implementing a condition-based maintenance strategy, the analysis of field data also helps to achieve these high reliability and availability goals. To decide, whether a system should be replaced or to estimate its situation on its lifetime curve, the analysis of field data is appropriate. This paper will present how failure data from maintenance interventions on the example of the Ventilation Gas Monitors (VGM) are used to estimate the system lifetime, failure rate and optimal point for exchange. A power law process is used to model the parametric growth curve of the number of failures and the Nelson-Aalen estimator is employed to model the non-parametric growth curve of the number of failures for repairable systems. The power law model is extrapolated and enhanced by its failure costs to make estimations about the necessary budget in the future and the optimal time for exchange. Additionally, confidence bounds and goodness-of-fit tests are included to evaluate the precision of the prediction. Taking advantage of open source software, a model with R language is established for all the calculations.
The first chapter of this paper gives an introduction to the topic. The second chapter outlines the functioning, structure and lifetime requirements of the VGM system and presents its collected failure data. The third chapter specifies the mathematical background for repairable system analysis, how the parameters are calculated with maximum likelihood estimation and describes the implementation of the Cramér-von Mises criterion and confidence bounds as goodness-of fit tests. The fourth chapter presents the results for the VGM system. The last chapter contains a conclusion and an outlook.
How to Cite
Repairable system analysis, Fielded systems data, Power law model, Maximum likelihood estimation, Non-homogenous Poisson process, Reliability analysis, Life cycle costs
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.