Reliability Analysis of Pressure Vessels in Lubricant Process Unit for Risk Based Inspection

2008 ◽  
Author(s):  
Chi-Hui Chien ◽  
Chun-Hung Chen
Keyword(s):  
Reliability Analysis ◽  
Pressure Vessel ◽  
Safety Concern ◽  
Limit State ◽  
Reliability Assessment ◽  
Pressure Vessels ◽  
State Function ◽  
Process Unit ◽  
Limit State Function ◽  
Failure Probabilities

As a safety concern to a pressurized system, to monitor the corrosion rate of each pressure vessel in order to make the repair decision at the right time based on the required thickness to withstand the maximum allowable working pressure (MAWP), is important to the plant owner. A plant inspector will normally assess the risk by evaluating the probability of failure of each pressure vessel during service hours with inspection and maintenance planning. Therefore, a scheme of reliability assessment to the pressure vessels should be established. The objective of this study is to discuss the failure probabilities of the pressure vessels in a lubricant unit in order to provide the input information for Risk Based Inspection (RBI) assessments. The reliability assessment of a pressure vessel involves the estimation of the failure pressure and evaluation of the limit state function. Based on the formula for calculating required thickness of a pressure vessel component, and due to the presence of non-linearity in the limit state function and the non-normal distributed variables, the first order second moment method (FOSM) was adopted for carrying out the reliability analysis. The uncertainties of the random variables in the limit state function were modeled by using normal and non-normal probabilistic distributions. As the heat exchanger is an important pressure vessel to a pressurized system, the failure probabilities together with the ranking categories of the heat exchangers in a lubricant unit are chosen as a case study to be discussed and presented in this paper.

scholarly journals Gaussian Process-Based Response Surface Method for Slope Reliability Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Hu ◽  
Guo-shao Su ◽  
Jianqing Jiang ◽  
Yilong Xiao
Keyword(s):  
Gaussian Process ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Surface Method ◽  
Gp Model

A new response surface method (RSM) for slope reliability analysis was proposed based on Gaussian process (GP) machine learning technology. The method involves the approximation of limit state function by the trained GP model and estimation of failure probability using the first-order reliability method (FORM). A small amount of training samples were firstly built by the limited equilibrium method for training the GP model. Then, the implicit limit state function of slope was approximated by the trained GP model. Thus, the implicit limit state function and its derivatives for slope stability analysis were approximated by the GP model with the explicit formulation. Furthermore, an iterative algorithm was presented to improve the precision of approximation of the limit state function at the region near the design point which contributes significantly to the failure probability. Results of four case studies including one nonslope and three slope problems indicate that the proposed method is more efficient to achieve reasonable accuracy for slope reliability analysis than the traditional RSM.

Iterative Algorithm for Structure Reliability Analysis Based on Support Vector Classification Method

2007 ◽  
Vol 353-358 ◽  
pp. 1009-1012
Author(s):  
Chao Ma ◽  
Zhen Zhou Lu
Keyword(s):  
Reliability Analysis ◽  
Iterative Algorithm ◽  
Limit State ◽  
Sampling Procedure ◽  
Support Vector ◽  
State Function ◽  
Limit State Function ◽  
Structure Reliability ◽  
Important Region ◽  
Actual Limit

For reliability analysis of structure with implicit limit state function, an iterative algorithm is presented on the basis of support vector classification machine. In the present method, the support vector classification machine is employed to construct surrogate of the implicit limit state function. By use of the proposed rational iteration and sampling procedure, the constructed support vector classification machine can converge to the actual limit state function at the important region, which contributes to the failure probability significantly. Then the precision of the reliability analysis is improved. The implementation of the presented method is given in detail, and the feasibility and the efficiency are demonstrated by the illustrations.

Risk Based Inspection Methodology for Components Subject to High-Temperature Creep

2017 ◽  
Author(s):  
Carl E. Jaske ◽  
Panos Topalis ◽  
Wong Sin Loong ◽  
Azura Sharina Md Sidek
Keyword(s):  
Limit State ◽  
Creep Damage ◽  
Mean Value ◽  
Pressure Vessels ◽  
State Function ◽  
Limit State Function ◽  
First Order ◽  
First Order Second Moment ◽  
Time In Service

Risk-based inspection (RBI) methodologies are widely used by industry to develop effective inspection programs for pressure vessels and piping. The RBI approach use data on equipment design, maintenance, and operation along with inspection history to evaluate both the likelihood and consequences of failure. RBI results provide a basis for selecting inspection methods and establishing inspection intervals and coverage. API RP 580 provides guidance on developing a RBI program for fixed equipment and piping, while API RP 581 provides quantitative procedures for establishing RBI methodology. Appendix J of the first edition (2000) of API RP 581 contained procedures for application to creep damage of furnace tubes. However, the second (2008) and third (2016) did not contain any procedures for application to creep damage of equipment, including furnace tubes. DNV GL undertook a RBI project for a coal-fired power plant in Malaysia that required evaluation of components subject to creep damage. As part of this project, a detailed likelihood of failure (LoF) model for creep was developed. This paper reviews the creep LoF model that was developed and discusses a case study of its application. The LoF is estimated using a limit state function where the resistance is characterized using Larson-Miller parameter creep-rupture expressions for the materials of interest and the load is characterized by the time in service. A mean value first order second moment (MVFOSM) method is employed to numerically compute LoF. Guidelines for including metallurgical replication results in the LoF estimate and for assigning inspection effectiveness for creep damage also are discussed.

Failure Assessment and Reliability Analysis of CFRP Composite Pressure Vessel

2007 ◽  
Vol 348-349 ◽  
pp. 225-228
Author(s):  
Jun Shen ◽  
M.L. Zhang ◽  
D.Y. Hou
Keyword(s):  
Reliability Analysis ◽  
Pressure Vessel ◽  
Progressive Failure ◽  
Limit State ◽  
Probability Of Failure ◽  
Fiber Direction ◽  
State Function ◽  
Design Variables ◽  
Composite Pressure Vessel ◽  
Cfrp Composite

A new approach for progressive failure and reliability analysis of carbon fiber reinforced polymeric (CFRP) composite pressure vessel with many base random variables is developed in the paper. The elastic constants of CFRP lamina and geometric parameters of the vessel are selected as the base design variables. CFRP lamina specimen and pressure vessel were manufactured and tested in order to obtain statistics of design variables. The limit state function for progressive failure analysis was set up. Then the progressive failure and reliability analysis of the vessel were performed according to the stiffness degradation model based on Monte Carlo simulation procedure using MATLAB. The distributions of failure loads and the probability of failure of the vessel were obtained. The feasibility and accuracy of the proposed method is validated by good agreement between the simulation and experimental results. Further analysis indicates that the lamina tensile strength in the fiber direction and hoop layer thickness of the vessel have significant influence on the probability of failure of composite pressure vessel.

scholarly journals Hybrid Reliability Analysis for Spacecraft Docking Lock with Random and Interval Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianguo Zhang ◽  
Jiwei Qiu ◽  
Pidong Wang
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
Simple Problem ◽  
State Function ◽  
Limit State Function ◽  
Interval Variables ◽  
Reliability Method ◽  
Hybrid Reliability

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis with hybrid variables, that is, random and interval variables. This method can significantly improve the computational efficiency for the abovementioned hybrid reliability analysis (HRA), while generally providing sufficient precision. In the proposed procedure, the hybrid problem is reduced to standard reliability problem with the polar coordinates, where an n-dimensional limit-state function is defined only in terms of two random variables. Firstly, the linear Taylor series is used to approximate the limit-state function around the design point. Subsequently, with the approximation of the n-dimensional limit-state function, the new bidimensional limit state is established by the polar coordinate transformation. And the probability density functions (PDFs) of the two variables can be obtained by the PDFs of random variables and bounds of interval variables. Then, the interval of failure probability is efficiently calculated by the integral method. At last, one simple problem with explicit expressions and one engineering application of spacecraft docking lock are employed to demonstrate the effectiveness of the proposed methods.

Application of Partial Safety Factors for Fitness-for-Service Assessment of Pressure Equipment With Local Metal Loss

2017 ◽  
Author(s):  
Takuyo Kaida ◽  
Shinsuke Sakai
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Probability Of Failure ◽  
Pressure Vessels ◽  
Metal Loss ◽  
State Function ◽  
Safety Factors ◽  
Loss Assessment ◽  
Reliability Method ◽  
Partial Safety Factors

Reliability analysis considering data uncertainties can be used to make a rational decision as to whether to run or repair a pressure equipment that contains a flaw. Especially, partial safety factors (PSF) method is one of the most useful reliability analysis procedure and considered in a Level 3 assessment of a crack-like flaw in API 579-1/ASME FFS-1:2016. High Pressure Institute of Japan (HPI) formed a committee to develop a HPI FFS standard including PSF method. To apply the PSF method effectively, the safety factors for each dominant variable should be prepared before the assessment. In this paper, PSF for metal loss assessment of typical pressure vessels are derived based on first order reliability method (FORM). First, a limit state function and stochastic properties of random variables are defined. The properties of a typical pressure vessel are based on actual data of towers in petroleum and petrochemical plants. Second, probability of failure in several cases are studied by Hasofer-Lind method. Finally, PSF’s in each target probability of failure are proposed. HPI published a new technical report, HPIS Z 109 TR:2016, that provide metal loss assessment procedures based on FORM and the proposed PSF’s described in this paper.

A Random Field Approach to Reliability Analysis With Random and Interval Variables

2015 ◽  
Vol 1 (4) ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Random Field ◽  
Reliability Analysis ◽  
Extreme Values ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Response Variable ◽  
Field Approach ◽  
Reliability Bounds ◽  
Interval Variables

Interval variables are commonly encountered in design, especially in the early design stages when data are limited. Thus, reliability analysis (RA) should deal with both interval and random variables and then predict the lower and upper bounds of reliability. The analysis is computationally intensive, because the global extreme values of a limit-state function with respect to interval variables must be obtained during the RA. In this work, a random field approach is proposed to reduce the computational cost with two major developments. The first development is the treatment of a response variable as a random field, which is spatially correlated at different locations of the interval variables. Equivalent reliability bounds are defined from a random field perspective. The definitions can avoid the direct use of the extreme values of the response. The second development is the employment of the first-order reliability method (FORM) to verify the feasibility of the random field modeling. This development results in a new random field method based on FORM. The new method converts a general response variable into a Gaussian field at its limit state and then builds surrogate models for the autocorrelation function and reliability index function with respect to interval variables. Then, Monte Carlo simulation is employed to estimate the reliability bounds without calling the original limit-state function. Good efficiency and accuracy are demonstrated through three examples.

Sequential Multidisciplinary Design Optimization and Reliability Analysis Using an Efficient Third-Moment Saddlepoint Approximation Method

2015 ◽  
Author(s):  
Debiao Meng ◽  
Hong-Zhong Huang ◽  
Huanwei Xu ◽  
Xiaoling Zhang ◽  
Yan-Feng Li
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Saddlepoint Approximation ◽  
Cumulative Distribution ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
State Function ◽  
Limit State Function ◽  
Highly Nonlinear ◽  
Third Moment

In Reliability based Multidisciplinary Design and Optimization (RBMDO), saddlepoint approximation has been utilized to improve reliability evaluation accuracy while sustaining high efficiency. However, it requires that not only involved random variables should be tractable; but also a saddlepoint can be obtained easily by solving the so-called saddlepoint equation. In practical engineering, a random variable may be intractable; or it is difficult to solve a highly nonlinear saddlepoint equation with complicated Cumulant Generating Function (CGF). To deal with these challenges, an efficient RBMDO method using Third-Moment Saddlepoint Approximation (TMSA) is proposed in this study. TMSA can construct a concise CGF using the first three statistical moments of a limit state function easily, and then express the probability density function and cumulative distribution function of the limit state function approximately using this concise CGF. To further improve the efficiency of RBMDO, a sequential optimization and reliability analysis strategy is also utilized and a formula of RBMDO using TMSA within the framework of SORA is proposed. Two examples are given to show the effectiveness of the proposed method.

scholarly journals A New SORM Method for Structural Reliability with Hybrid Uncertain Variables

2020 ◽  
Vol 11 (1) ◽  
pp. 346
Author(s):  
Pidong Wang ◽  
Lechang Yang ◽  
Ning Zhao ◽  
Lefei Li ◽  
Dan Wang
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
State Function ◽  
Strong Nonlinearity ◽  
Limit State Function ◽  
Practical Applications ◽  
Uncertain Variables ◽  
Practical Engineering

(1) Background: in practical applications, probabilistic and non-probabilistic information often simultaneously exit. For a complex system with a nonlinear limit-state function, the analysis and evaluation of the reliability are imperative yet challenging tasks. (2) Methods: an improved second-order method is proposed for reliability analysis in the presence of both random and interval variables, where a novel polar transformation is employed. This method enables a unified reliability analysis taking both random variables and bounded intervals into account, simplifying the calculation by transforming a high-dimension limit-state function into a bivariate state function. The obtained nonlinear probability density functions of two variables in the function inherit the statistic characteristics of interval and random variables. The proposed method does not require any strong assumptions and so it can be used in various practical engineering applications. (3) Results: the proposed method is validated via two numerical examples. A comparative study towards a contemporary algorithm in state-of-the-art literature is carried out to demonstrate the benefits of our method. (4) Conclusions: the proposed method outperforms existing methods both in efficiency and accuracy, especially for cases with strong nonlinearity.

Sign in / Sign up

Export Citation Format

Share Document