Probabilistic Assessment of ILI Metal Loss Features

2010 ◽  
Author(s):  
Peter D. Chan ◽  
David Webster
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
New Method ◽  
Point Estimate ◽  
Metal Loss ◽  
Probabilistic Assessment ◽  
Maintenance Planning ◽  
Point Estimate Method ◽  
Estimate Method ◽  
Normal Method

In-line inspection (ILI) gives pipeline operators a snapshot of the condition of their pipelines. Unfortunately, accuracy limitations exist with all ILI tools. It is therefore judicious for pipeline operators to support their pipeline integrity maintenance planning by employing Probability of Exceedance (POE) methodology that accounts for the inexact nature of the ILI data. A new method is needed that can be used to assist pipeline operators make rational and defensible integrity decision when faced with very poor pipeline conditions with numerous interacting metal loss defects. The normal method of using RSTRENG with Monte Carlo Simulation (MC) for probabilistic assessment of corroded pipeline pressure is unsuitable and unmanageable. The new method employs DNV-RP-F101 with Point Estimate Method (PE) and the MC to efficiently apply the POE methodology to severely corroded pipelines.

scholarly journals Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

2019 ◽  
Vol 233 (5) ◽  
pp. 761-774
Author(s):  
Thorsten Neumann ◽  
Beate Dutschk ◽  
René Schenkendorf
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Asset Management ◽  
Failure Detection ◽  
Point Estimate ◽  
Point Estimate Method ◽  
Model Response ◽  
Track Condition ◽  
Estimate Method ◽  
Sample Points

Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model response that must be analyzed carefully to avoid misleading results and conclusions. Commonly, Monte Carlo simulations are used for such analyses which often require a large number of sample points to be evaluated for convergence. Moreover, even if quite close to the exact distributions, the Monte Carlo approach necessarily provides approximate results only. In contrast to that, the present contribution reviews an alternative way of computing important statistical quantities of the model response. The so-called point estimate method, which can be shown to be exact under certain constraints, usually (i.e. depending on the number of input variables) works with only a few specific sample points. Thus, the point estimate method helps to reduce the computational load for model evaluation considerably in the case of complex models or large-scale applications. To demonstrate the point estimate method, five academic but typical examples of railway asset management are analyzed in more detail: (a) track degradation, (b) reliability analysis of composite systems, (c) terminal reliability in rail networks, (d) failure detection/identification using decision trees, and (e) track condition modeling incorporating maintenance. Advantages as well as limitations of the point estimate method in comparison with common Monte Carlo simulations are discussed.

Sign in / Sign up

Export Citation Format

Share Document