An Efficient Methodology for the Reliability Analysis of Corroding Pipelines

2012 ◽  
Author(s):  
S. Zhang ◽  
W. Zhou
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Weighting Factor ◽  
Probability Weighting ◽  
Index Method ◽  
First Order ◽  
Corrosion Defect ◽  
Small Leak ◽  
Reliability Method ◽  
Dependent Failure

This paper describes an efficient methodology that utilizes the first order reliability method (FORM) and system reliability approaches to evaluate the time-dependent failure probabilities of a pressurized pipeline at a single active corrosion defect considering three different failure modes, i.e. small leak, large leak and rupture. The criteria for distinguishing small leak, large leak and rupture at a given corrosion defect are established based on the information in the literature. The wedge integral and probability weighting factor methods are used to evaluate the probabilities of small leak and burst, whereas the conditional reliability index method is used to evaluate the probabilities of large leak and rupture. Two numerical examples are used to illustrate the accuracy, efficiency and robustness of the proposed methodology. The proposed methodology can be used to facilitate reliability-based corrosion management programs for energy pipelines.

An Efficient Methodology for the Reliability Analysis of Corroding Pipelines

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Shenwei Zhang ◽  
Wenxing Zhou
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Weighting Factor ◽  
Probability Weighting ◽  
Index Method ◽  
First Order ◽  
Corrosion Defect ◽  
Small Leak ◽  
Reliability Method ◽  
Dependent Failure

This paper describes an efficient methodology that utilizes the first order reliability method (FORM) and system reliability approaches to evaluate the time-dependent failure probabilities of a pressurized pipeline at a single active corrosion defect considering three different failure modes, i.e., small leak, large leak, and rupture. The criteria for distinguishing small leak, large leak, and rupture at a given corrosion defect are established based on the information in the literature. The wedge integral and probability weighting factor methods are used to evaluate the probabilities of small leak and burst, whereas the conditional reliability index method is used to evaluate the probabilities of large leak and rupture. Two numerical examples are used to illustrate the accuracy, efficiency and robustness of the proposed methodology. The proposed methodology can be used to facilitate reliability-based corrosion management programs for energy pipelines.

System Reliability of Existing Jacket Platform in Malaysian Water (Failure Path and System Reliability Index)

2014 ◽  
Vol 567 ◽  
pp. 307-312 ◽  
Author(s):  
V. John Kurian ◽  
Mohamed Mubarak Abdul Wahab ◽  
T.S. Kheang ◽  
Mohd Shahir Liew
Keyword(s):  
System Reliability ◽  
Reliability Index ◽  
Structural Reliability ◽  
Pushover Analysis ◽  
Probability Of Failure ◽  
Jacket Platform ◽  
First Order ◽  
Failure Path ◽  
Reliability Method ◽  
Two Parameters

The objective of this work is to determine the structural reliability of an existing jacket platform in Malaysia, by determining the system probability of failure and its corresponding reliability index. These two parameters are important indicators for assessing the integrity and reliability of the platform, and will point out whether the platform is suitable for continued operation. In this study, pushover analysis is used to determine possible failure paths of the structure, while First Order Reliability Method (FORM) and Simple Bound Formula are used to determine the failure probability and reliability index. Three failure paths of the platform are established. The reliability index of these paths is found with the highest Reliability Indexto be 18.82 from the 315-degree path, while the system reliability index is 9.23. This illustrates that the platform is robust and the chances of collapse is very small.

System Reliability Analysis With Autocorrelated Kriging Predictions

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Hao Wu ◽  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Limit State ◽  
Surrogate Models ◽  
First Order ◽  
Highly Nonlinear ◽  
Reliability Method ◽  
Reliability Methods ◽  
Computationally Intensive ◽  
Improved Accuracy ◽  
State Functions

Abstract When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples.

scholarly journals A moment estimation–based method for the path planning of manipulators with kinematic system reliability constraints

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881689
Author(s):  
Jin Wang ◽  
Wei Wang ◽  
Jianhui Fu ◽  
Guodong Lu
Keyword(s):  
Path Planning ◽  
System Reliability ◽  
Maximum Entropy Principle ◽  
Extreme Value Distribution ◽  
Linearization Method ◽  
Research Field ◽  
First Order ◽  
Moment Estimation ◽  
Reliability Method ◽  
First Four Moments

Since the path planning plays a significant role in the manipulator’s control, the system reliability evaluation and optimization for path planning are studied with consideration of the joint clearance. A simple moment estimation–based method is proposed; a linear performance function is first established using the extreme value distribution theory. Based on the maximum entropy principle, the first four moments of variables are utilized to derive a best-fit probability density function to feature the characteristics of the system distribution rather than an empirical assumption of the normality. To meet the system reliability criterion constraints, a sensitivity analysis is conducted using the direct linearization method. With modification in the tolerance of sensitive parameters, the reliability can be improved efficiently. Traditional methods, such as the first-order second moment method, the first-order reliability method, and Monte Carlo simulation, are popular in this research field and therefore they are applied as benchmark methods for comprehensive comparisons in the accuracy and efficiency. A typical serial manipulator is applied as an example to validate the feasibility of our proposed method.

Time-Dependent Reliability Analysis for Bivariate Responses

2015 ◽  
Author(s):  
Zhen Hu ◽  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
General System ◽  
Time Dependent ◽  
Series System ◽  
Analysis Method ◽  
First Order ◽  
Reliability Method ◽  
General Systems ◽  
Bivariate Responses

Time-dependent system reliability is measured by the probability that the responses of a system do not exceed prescribed failure thresholds over a period of time. In this work, an efficient time-dependent reliability analysis method is developed for bivariate responses that are general functions of random variables and stochastic processes. The proposed method is based on single and joint upcrossing rates, which are calculated by the First Order Reliability Method (FORM). The method can efficiently produce accurate upcrossing rates for the systems with two responses. The upcrossing rates can then be used for system reliability predictions with two responses. As the general system reliability may be approximated with the results from reliability analyses for individual responses and bivariate responses, the proposed method can be extended to reliability analysis for general systems with more than two responses. Two examples, including a parallel system and a series system, are presented.

A System Reliability Method With Dependent Kriging Predictions

2016 ◽  
Author(s):  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Limit State ◽  
Surrogate Models ◽  
First Order ◽  
Highly Nonlinear ◽  
Reliability Method ◽  
Reliability Methods ◽  
Computationally Intensive ◽  
Improved Accuracy ◽  
State Functions

When limit-state functions are highly nonlinear, traditional reliability methods, such as the first order and second order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used, but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Cheaper surrogate models are created for limit-state functions with the minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the dependence between responses from a surrogate model, this method captures the true contribution of each MCS sample to the uncertainty in the estimate of the system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by three examples.

First-Order Reliability Analysis of Vehicle Safety in Highway Horizontal Curves

2013 ◽  
Author(s):  
Jaekwan Shin ◽  
Ikjin Lee
Keyword(s):  
Reliability Analysis ◽  
Failure Modes ◽  
Design Method ◽  
First Order ◽  
Horizontal Curves ◽  
State Highway ◽  
Reliability Method ◽  
Current Design ◽  
Margin Of Safety ◽  
Analytic Models

This study presents a reliability analysis of vehicle sideslip and rollover in highway horizontal curves, mainly focusing on exit ramps and interchanges. To accurately describe failure modes of a ground vehicle, analytic models for sideslip and rollover are derived considering nonlinear characteristics of vehicle behavior using the commercial software, TruckSim®, with high fidelity. Then, the probability of accident is evaluated using the First-Order Reliability Method (FORM). Furthermore, sensitivity functions of each failure mode are analytically derived to apply FORM. Numerical studies are conducted using a single-unit truck model. The results show that a truck is more likely to rollover than to slip at dry load. To propose practical application of the study, the reliability analysis for the minimum radius recommended by American Association of State Highway and Transportation Officials (AASHTO) at various speeds and bank angles is conducted. The reliability analysis of current design method shows that the method cannot provide the sufficient margin of safety against both of rollover and sideslip when there are deviations from assumed conditions, especially at low speed of vehicles.

System Reliability Evaluation Based on Stochastic Limit Analysis

2012 ◽  
Vol 256-259 ◽  
pp. 1144-1147
Author(s):  
Bo Yu ◽  
Lu Feng Yang
Keyword(s):  
Limit Analysis ◽  
System Reliability ◽  
Failure Modes ◽  
Limit State ◽  
Limit Load ◽  
Frame Structure ◽  
Mode Analysis ◽  
Stochastic Limit ◽  
Structural Resistance ◽  
Reliability Method

It is quite difficult to estimate system reliability of complex structures which involve lots of potential failure modes. A novel method for system reliability analysis of frame structure was proposed based on stochastic limit analysis. Stochastic limit load was firstly calculated based on the elastic modulus reduction method (EMRM) by taking the uncertainty of structural resistance into account. Once the limit state equation was established by the stochastic limit load, the system reliability index can be calculated by the first-order reliability method (FORM) efficiently. Numerical example illustrates the efficiency and accuracy of the proposed method by comparing with the traditional failure mode analysis approaches.

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