Quantitative risk prognostics framework based on Petri Net and Bow-Tie models

Vileiniskis, Marius and Remenyte-Prescott, Rasa (2017) Quantitative risk prognostics framework based on Petri Net and Bow-Tie models. Reliability Engineering and System Safety, 165 . pp. 62-73. ISSN 0951-8320

[img] PDF - Repository staff only until 23 March 2019. - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution Non-commercial No Derivatives.
Download (823kB)

Abstract

A simulation framework based on the Petri Net model is proposed in this paper used for performing quantitative risk prognosis through extending the Bow-Tie model. A Petri Net model is built to include features, specific to assets, such as the condition of the asset, the projected operational usage, inspection and maintenance policies and degradation process, so that the future condition of the asset over time can be estimated. Several new Petri Net modelling features which advance the traditional Bow-Tie approach are proposed, such as asset usage generating and usage dependent transitions, and the possibility of entering evidence about the actual condition of the asset through the use of truncated distributions. Monte Carlo simulation method is used to simulate the developed Petri Net model over a selected time frame, in order to obtain statistics necessary to perform risk assessment using the Bow-Tie model. The paper reports on the overall proposed methodology and then focusses on the development of the Petri Net model. The methodology is applied in risk prognostics of operating an underground passenger lift. In particular, the combination of the Petri Net and the Bow-Tie models is illustrated to predict the likelihood and the consequences of an event when a lift gets stuck in a shaft between landings.

Item Type: Article
Keywords: Petri Net; Bow-Tie model; Fault tree; Event tree; risk; asset management; prognostics
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
Identification Number: 10.1016/j.ress.2017.03.026
Depositing User: Eprints, Support
Date Deposited: 24 Mar 2017 14:07
Last Modified: 13 Oct 2017 01:18
URI: http://eprints.nottingham.ac.uk/id/eprint/41560

Actions (Archive Staff Only)

Edit View Edit View