Developing a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Network |
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| Institution: | 1. Department of Industrial Engineering (DIEF), University of Florence, 50125, Florence, Italy;2. IBISLAB, University of Florence, 50125, Florence, Italy;3. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology NTNU, S.P. Andersens Veg 5, 7031, Trondheim, Norway;1. Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran;2. Department of Industrial Engineering, Shahed University, Tehran, Iran;1. University of Ljubljana, Faculty of Mechanical Engineering, A?ker?eva 6, 1000 Ljubljana, Slovenia;2. Plinovodi d.o.o., Cesta Ljubljanske brigade 11b, 1000 Ljubljana, Slovenia;1. School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, 510006, China;2. School of Electro-mechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China;1. Department of Industrial Engineering (DIEF), University of Florence, Florence, Italy;2. Renewable Energy Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn, Cornwall, TR10 9FE, UK;3. Department of Civil Engineering, University of Parsian, Qazvin, Iran;4. School of Engineering, Macquarie University, Sydney, NSW, Australia;1. Sustainable Development and Environmental Economics Office, Department of Environment, Iran;2. Faculty of Environmental Studies, Universiti Putra Malaysia, Malaysia;3. Board of Directors, Saba Rig Providing Company, Iran |
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| Abstract: | During the last decades, the vital role of maintenance activities in industries including natural gas distribution system has cleared up progressively. High costs may induce to reduced maintenance and, in turn, lead to a lower availability and high risk of undesired events. Therefore, a probabilistic model, based on an acceptable level of risk, is required to avoid under and over estimation of maintenance time interval. This paper presents an advanced Risk-based Maintenance (RBM) methodology to optimize maintenance time schedule. Bayesian Network (BN) is applied to model the risk and the associated uncertainty. The developed method can assist the asset managers to work out the exact maintenance time for each component according to the risk level. To demonstrate and discuss the applicability of the methodology, a case study of Natural Gas Reduction and Measuring Station in Italy is considered. Results prove that the most critical components are the calculator and pilots, while the most reliable one is the odorization. Furthermore, the pressure and temperature gauge (PTG), the remote control system (RCS) and the meter are predicted as the components that require less time to transit from minor risk to catastrophic risk. |
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| Keywords: | Risk-based maintenance Bayesian Network Natural gas reduction and measuring station |
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