Risk-Based Integrity and Inspection Modeling (RBIIM) of Process Components/System



This article is corrected by:

  1. Errata: Erratum to “Risk-Based Integrity and Inspection Modeling (RBIIM) of Process Components/System,” by Faisal I. Khan, Mahmoud M. Haddara, and Subrata K. Bhattacharya, in Risk Analysis, 26(1), 2006 Volume 27, Issue 1, 283, Article first published online: February 2007

* Address correspondence to Faisal I. Khan, Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John's, NL, A1B 3X5, Canada; fkhan@engr.mun.ca.


Process plants deal with hazardous (highly flammable and toxic) chemicals at extreme conditions of temperature and pressure. Proper inspection and maintenance of these facilities is paramount for the maintenance of safe and continuous operation. This article proposes a risk-based methodology for integrity and inspection modeling (RBIIM) to ensure safe and fault-free operation of the facility. This methodology uses a gamma distribution to model the material degradation and a Bayesian updating method to improve the distribution based on actual inspection results. The method deals with the two cases of perfect and imperfect inspections. The measurement error resulting from imperfect inspections is modeled as a zero-mean, normally distributed random process. The risk is calculated using the probability of failure and the consequence is assessed in terms of cost as a function of time. The risk function is used to determine an optimal inspection and replacement interval. The calculated inspection and replacement interval is subsequently used in the design of an integrity inspection plan. Two case studies are presented: the maintenance of an autoclave and the maintenance of a pipeline segment. For the autoclave, the interval between two successive inspections is found to be 19 years. For the pipeline, the next inspection is due after 5 years from now. Measurements taken at inspections are used in estimating a new degradation rate that can then be used to update the failure distribution function.