| 1. |
- Norrbin, Per, et al.
(författare)
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Infrastructure robustness for railway systems
- 2016
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Ingår i: International Journal of Performability Engineering. - 0973-1318. ; 12:3, s. 249-264
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Tidskriftsartikel (refereegranskat)abstract
- In the railway industry, most maintenance approaches are based on certain “specified conditions”, e.g., RAMS (Reliability, Availability, Maintainability and Safety) and Risk. But the reality is more complex. Instead of the assumed conditions, “unfavorable conditions” may occur from either natural or operational causes, where robustness can be an effective approach. To adequately consider “unfavorable conditions” and to reduce “uncertainties” in railway maintenance, this study conducts a holistic examination of railway infrastructure robustness. It gives an overview of robustness and discusses some relevant studies. It then develops a new road map for railway infrastructure robustness, including a novel definition and a new framework of robustness management, based on continuous improvement. It explores the opportunities of applying the road map to the infrastructure of railway systems and outlines some practical concerns and remaining challenges for future research. The results provide guidelines for other research into robust infrastructure in railway maintenance.
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| 2. |
- Saari, Esi, et al.
(författare)
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Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data
- 2019
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Ingår i: International Journal of Performability Engineering. - : Totem Publisher, Inc.. - 0973-1318. ; 15:5, s. 1314-1325
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Tidskriftsartikel (refereegranskat)abstract
- Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined by a Bayesian Weibull model and a Bayesian lognormal model, respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.
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