Asset Integrity Modelling and Management
In all process facilities, asset’s integrity changes over time due to use, repair, material degradation and accidental damage. These changes need to be monitored and understood to ensure continued safe operations. The likelihood that an asset will continue to perform its function can be evaluated through fitness-for-service assessments. These assessments evaluate asset life before a component is put into service to assess manufacturing or after installation to assess in-service damage. These assessments are used to determine the risk of failure and determine a course of action. Return to service, repair, replace, or increase monitoring.
One way to manage asset integrity is through implementation of Risk-Based Inspection (RBI). This methodology develops inspection and maintenance plans based on risk. Risk is defined through analysis of the probability of an incident occurring and the severity of the consequences if an incident does occur. Risk-based inspection helps to focus inspection resources on key areas, evaluate the system-wide risk against an operator set risk acceptance criteria, and develop optimal maintenance strategies.
RBI is key to maintain safe and productive facility. This unit of the SREG is committed to develop improved method integrity assessments, degradation modelling, developing design, inspection and maintenance strategies, improving inventory management promote sustainable operations, and develop novel method to optimize maintenance strategies.
- Asset Integrity Management in Harsh Environments
- Predictive models for material degradation and develop a risk-based remaining life assessment for assets under CUI attack.
- Asset Integrity Modeling with limited information
- Assessment of the integrity of protection systems
- Harsh Environment Inspection
- Risk-Based Inspections in extreme conditions
- Understanding changes in Human factors in extreme conditions
Principal Research Lead:
Project title: Understanding Corrosion Under Insulation(CUI) in Harsh Arctic Environments to Develop Risk-based Life Assessment for Assets under CUI Attack.
Project description: This work will develop and conduct field and accelerated experiments to develop more a accurate CUI rate model that will be used in risk based life assessment.
Related Publications since 2010:
- M Khalifa, F Khan, M Haddara (2013). Inspection sampling of pitting corrosion. Insight-Non-Destructive Testing and Condition Monitoring 55 (6), 290-296
- P Thodi, F Khan, M Haddara (2013). Risk based integrity modeling of offshore process components suffering stochastic degradation. Journal of Quality in Maintenance Engineering 19 (2), 157-180
- Hasan, S., Khan, F., and Kenny, S. (2012). Probability assessment of burst limit state due to internal corrosion. International journal of pressure vessel and piping, 89, 48 - 58
- Keshavarz, G., Thodi, P., and Khan, F. (2012). Risk Based Shutdown Management of LNG Units, Journal of Loss Prevention in Process Industries, 25 (1), 159 –165
- Khalifa, M., Khan, F., and Haddara, M. (2012) Bayesian sample size determination for inspection of general corrosion of process components. Journal of Loss Prevention in the Process Industry, 25. 218 - 223
- Khalifa, M., Khan, F., and Haddara, M. (2012) A Methodology for calculating sample size to assess localized corrosion for process components. Journal of Loss Prevention in the Process Industry, 25. 70 – 80
- Hassan, J. and Khan, F. (2012). Asset Integrity Management Indicator System. Journal of Loss Prevention In Process Industries, 25. 544 – 554
- Hasan, M., Khan, F., and Kenny, S. (2011). Identification of the cause of variability of probability of failure for different codes and standards. Journal of Pressure Vessel Technology, 133(4).
- Khalifa, M., Khan, F., Haddara, M. (2010) Reliability based inspection of nickel-based alloy welds in boiling water reactor environment. Reliability Engineering and System Safety, 95(5). 494-498.
- Thodi, P., Khan, F., and Haddara, M. (2010). The development of posterior probability models for risk based integrity modeling. Risk Analysis, 30, 3. 400-420