Scipediacontent: Scipediacontent moved page Draft Content 640721598 to Patelli Angelis 2018b

2021-01-25T09:45:58Z

Scipediacontent moved page Draft Content 640721598 to Patelli Angelis 2018b

Scipediacontent: Created page with " == Abstract == The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and..."

2021-01-25T09:45:55Z

Created page with " == Abstract == The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and..."

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== Abstract ==

The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and networks via an effective maintenance policy. However, simplifications and assumptions are usually adopted to compensate lack of data, imprecision and vagueness, which cannot be justified completely and may, thus lead to biased results. To overcome these issues, an imprecise probabilities approach is proposed for reliability analysis and risk-based maintenance strategy. A novel efficient computational approach is proposed for identifying robust maintenance strategies. The optimal solution is obtained through only one reliability assessment based on Advanced Line Sampling and reusing the outcome of maintenance activities in a force Monte Carlo approach. The proposed methodology remove the huge computational cost of reliability-base optimization making the analysis of industrial size problem feasible. The applicability of the approach is demonstrated by identifying the optimal maintenance policy of buried pipelines and it is shown how this approach can improve the current industrial practice.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_640721598-beopen1044-4936-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1201/9781351174664-276 http://dx.doi.org/10.1201/9781351174664-276] under the license http://creativecommons.org/licenses/by-nc-nd/4.0

* [https://strathprints.strath.ac.uk/71771/1/Patelli_de_Angelis_TF_2018_An_efficient_computational_strategy_for_robust_maintenance_scheduling.pdf https://strathprints.strath.ac.uk/71771/1/Patelli_de_Angelis_TF_2018_An_efficient_computational_strategy_for_robust_maintenance_scheduling.pdf]

* [https://www.taylorfrancis.com/books/e/9781351174664/chapters/10.1201/9781351174664-276 https://www.taylorfrancis.com/books/e/9781351174664/chapters/10.1201/9781351174664-276],
: [https://www.scipedia.com/public/Patelli_Angelis_2018a https://www.scipedia.com/public/Patelli_Angelis_2018a],
: [https://strathprints.strath.ac.uk/71771 https://strathprints.strath.ac.uk/71771],
: [https://pureportal.strath.ac.uk/en/publications/an-efficient-computational-strategy-for-robust-maintenance-schedu https://pureportal.strath.ac.uk/en/publications/an-efficient-computational-strategy-for-robust-maintenance-schedu],
: [https://academic.microsoft.com/#/detail/2905367666 https://academic.microsoft.com/#/detail/2905367666] under the license http://creativecommons.org/licenses/by-nc-nd/4.0

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Patelli Angelis 2018b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 640721598 to Patelli Angelis 2018b

Scipediacontent: Created page with " == Abstract == The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and..."