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51316-7070-Reliability Assessment Of Offshore Asset Under Pitting Corrosion

Corrosion is a major cause of structural deterioration in the marine/offshore industry.  FOr that reason, reliability assessment and maintenance planning of these structures are crucial. In the current work a combination multi-phase phenomenological and mechanistic model for pitting corrosion is tested using Bayesian network (BN) approach.

Product Number: 51316-7070-SG
ISBN: 7070 2016 CP
Author: Jyoti Bhandari
Publication Date: 2016
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Reliability Assessment of offshore Asset under pitting corrosionJyoti Bhandari a Faisal Khanab Rouzbeh Abbassia Vikram Garaniyaa and Roberto Ojedaaa Offshore Environmental Safety and Risk Engineering (OESRE) Australian Maritime College University of Tasmania Launceston. TAS 7250 Australiab Safety and Risk Engineering Group (SREG) Faculty of Engineering & Applied Science Memorial University of Newfoundland St. John’s Newfoundland A1B 3X5 CanadaABSTRACTCorrosion is a major cause of structural deterioration in marine and offshore industries. It affects the life of process equipment and pipelines resulting in structural failure leakage product loss environment pollution and the loss of life. Pitting corrosion is regarded as one of the most hazardous form of corrosion in marine and offshore structures. Hence reliability assessment and maintenance planning of these structures are extremely crucial.The empirical and statistical degradation models are developed by either fitting field or lab data. These models though are useful for specific site or operating conditions but still carry high uncertainty. The other modelling approaches used in industry are phenomenological models which are based corrosion scientific principle. These models provide strong understanding however often are hard to test in engineering applications.In current work a combination of multi-phase phenomenological and mechanistic model for pitting corrosion is tested using Bayesian network (BN) approach. The BN is also used as a tool to model reliability of the marine and offshore structure susceptible of pitting corrosion. BN allows estimating likelihood of rare failure events of complex structures in an efficient way. It also assists to update the prediction using new information available through measurements monitoring and inspection. The present study demonstrates the application of BN models on complex steel structures.

Keywords: downloadabble, Pitting corrosion, Phenomenological model, Bayesian Network, Reliability analysis

Reliability Assessment of offshore Asset under pitting corrosionJyoti Bhandari a Faisal Khanab Rouzbeh Abbassia Vikram Garaniyaa and Roberto Ojedaaa Offshore Environmental Safety and Risk Engineering (OESRE) Australian Maritime College University of Tasmania Launceston. TAS 7250 Australiab Safety and Risk Engineering Group (SREG) Faculty of Engineering & Applied Science Memorial University of Newfoundland St. John’s Newfoundland A1B 3X5 CanadaABSTRACTCorrosion is a major cause of structural deterioration in marine and offshore industries. It affects the life of process equipment and pipelines resulting in structural failure leakage product loss environment pollution and the loss of life. Pitting corrosion is regarded as one of the most hazardous form of corrosion in marine and offshore structures. Hence reliability assessment and maintenance planning of these structures are extremely crucial.The empirical and statistical degradation models are developed by either fitting field or lab data. These models though are useful for specific site or operating conditions but still carry high uncertainty. The other modelling approaches used in industry are phenomenological models which are based corrosion scientific principle. These models provide strong understanding however often are hard to test in engineering applications.In current work a combination of multi-phase phenomenological and mechanistic model for pitting corrosion is tested using Bayesian network (BN) approach. The BN is also used as a tool to model reliability of the marine and offshore structure susceptible of pitting corrosion. BN allows estimating likelihood of rare failure events of complex structures in an efficient way. It also assists to update the prediction using new information available through measurements monitoring and inspection. The present study demonstrates the application of BN models on complex steel structures.

Keywords: downloadabble, Pitting corrosion, Phenomenological model, Bayesian Network, Reliability analysis

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