Quantitative risk-based inspection approach for high-energy piping using a probability distribution function and modification factor

Risk-based inspection (RBI) offers the most cost-effective operation and maintenance strategy for minimizing overall risks and making financial and safety improvements within a power plant. Risk assessment may also benefit decision-making with regards to plant design and equipment manufacturing processes. The purpose of this study is to propose a quantitative RBI methodology that can be applied to a high-energy piping system operating at elevated temperatures and high pressures in power plants. Three major steps involved are: (i) using generic mean time to failure (MTTF), specified as the expected lifetime of the equipment obtained from a theoretical equation; (ii) adding a modification factor that identifies the specific conditions that can influence the failure rate; and (iii) incorporating individual inspection locations as a factor in risk evaluation. Probability-of-failure (POF) assessment was conducted using a Weibull distribution with MTTF and the modification factor. In addition, consequence-of-failure (COF) assessment was conducted based on financial consequences in terms of downtime costs per day and the length of downtime due to failure. The main steam piping system in a thermal power plant was selected as the case study unit. The actual inspection locations where damage to components could frequently occur were defined as risk evaluation targets for the RBI. The generic MTTF for the main steam piping system was evaluated using the Larson-Miller parameter (LMP) equation for creep rupture time. Piping system stress analysis was conducted to obtain the stress applied to the LMP. An equation for the modification factor applied to the main steam piping system was also proposed based on five subfactors.

Automated summary

This study introduces a quantitative Risk-Based Inspection (RBI) methodology for high-energy piping systems in power plants, involving risk assessment, probability-of-failure assessment, and consequence-of-failure assessment. The research also discusses specific inspection locations, equipment modification factors, and the evaluation of generic mean time to failure (MTTF) based on the Larson-Miller parameter (LMP) equation.