A Partial Safety Factor Method for System Reliability Prediction With Outsourced Components

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhengwei Hu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Joint Probability ◽  
System Level ◽  
Safety Factors ◽  
Component Reliability ◽  
Reliability Method ◽  
Component Supplier ◽  
Partial Safety Factors ◽  
Joint Probability Density ◽  
Probability Density Function Pdf

System reliability is usually predicted with the assumption that all component states are independent. This assumption may not accurate for systems with outsourced components since their states are strongly dependent and component details may be unknown. The purpose of this study is to develop an accurate system reliability method that can produce complete joint probability density function (PDF) of all the component states, thereby leading to accurate system reliability predictions. The proposed method works for systems whose failures are caused by excessive loading. In addition to the component reliability, system designers also ask for partial safety factors for shared loadings from component suppliers. The information is then sufficient for building a system-level joint PDF. Algorithms are designed for a component supplier to generate partial safety factors. The method enables accurate system reliability predictions without requiring proprietary information from component suppliers.

A Partial Safety Factor Method for System Reliability Prediction With Outsourced Components

2018 ◽  
Author(s):  
Zhengwei Hu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Joint Probability ◽  
System Level ◽  
Reliability Prediction ◽  
Safety Factors ◽  
Component Reliability ◽  
Reliability Method ◽  
Component Supplier ◽  
Partial Safety Factors ◽  
Joint Probability Density

System reliability is usually predicted with the assumption that all component states are independent. This assumption is particularly useful for systems with outsourced components. The assumption, however, may produce large errors in the system reliability prediction since many component states are strongly dependent. The purpose of this study is to develop an accurate system reliability method that can produce complete joint probability density function (PDF) of all the component states, thereby leading to accurate system reliability predictions. The proposed method works for systems whose failures are caused by excessive loading. In addition to the component reliability, system designers also ask for partial safety factors for shared loadings from component suppliers. The information is then sufficient for building a system-level joint PDF. Algorithms are designed for a component supplier to generate partial safety factors, which enables accurate system reliability predictions without requiring proprietary information from component suppliers.

Recovering Missing Component Dependence for System Reliability Prediction via Synergy between Physics and Data

2021 ◽  
pp. 1-39
Author(s):  
Huiru Li ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Traditional Method ◽  
Joint Probability ◽  
Systems Design ◽  
Physical Models ◽  
Design System ◽  
Model Parameters ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems

Abstract Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.

scholarly journals Integration of Statistics- and Physics-Based Methods—A Feasibility Study on Accurate System Reliability Prediction

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Zhengwei Hu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Learning Strategy ◽  
Limit State ◽  
Joint Probability ◽  
Reliability Prediction ◽  
Support Vector ◽  
State Function ◽  
Component Reliability ◽  
Reliability Method ◽  
State Functions

Component reliability can be estimated by either statistics-based methods with data or physics-based methods with models. Both types of methods are usually independently applied, making it difficult to estimate the joint probability density of component states, which is a necessity for an accurate system reliability prediction. The objective of this study is to investigate the feasibility of integrating statistics- and physics-based methods for system reliability analysis. The proposed method employs the first-order reliability method (FORM) directly for a component whose reliability is estimated by a physics-based method. For a component whose reliability is estimated by a statistics-based method, the proposed method applies a supervised learning strategy through support vector machines (SVM) to infer a linear limit-state function that reveals the relationship between component states and basic random variables. With the integration of statistics- and physics-based methods, the limit-state functions of all the components in the system will then be available. As a result, it is possible to predict the system reliability accurately with all the limit-state functions obtained from both statistics- and physics-based reliability methods.

Analysis of the Partial Safety Factor Method Using Probabilistic Techniques

2010 ◽  
Author(s):  
B. A. Lindley ◽  
P. M. James
Keyword(s):  
Probabilistic Method ◽  
Probability Of Failure ◽  
Safety Factors ◽  
First Order Reliability Method ◽  
First Order ◽  
Target Probability ◽  
Reliability Method ◽  
Input Parameters ◽  
Partial Safety Factors ◽  
Hand Calculation

Partial Safety Factors (PSFs) are scaling factors which are used to modify the input parameters to a deterministic fracture mechanics assessment in order to consider the effects of variability or uncertainty in the values of the input parameters. BS7910 and SINTAP have adopted the technique, both of which use the First Order Reliability Method (FORM) to derive values for PSFs. The PSFs are tabulated, varying with the target probability of failure, p(F), and the Coefficient of Variance (COV) of the variable. An accurate assessment of p(F) requires a probabilistic method with enough simulations. This has previously been found to be time consuming, due to the large number of simulations required. The PSF method has been seen as a quick way of calculating an approximate, conservative value of p(F). This paper contains a review of the PSF method, conducted using an efficient probabilistic method called the Hybrid probabilistic method. The Hybrid probabilistic method is used to find p(F) at a large number of assessment points, for a range of different PSFs. These p(F) values are compared to those obtained using the PSF method. It is found that the PSF method was usually, and often extremely, conservative. However there are also cases where the PSF method was non-conservative. This result is verified by a hand calculation. Modifications to the PSF method are suggested, including the establishment of a minimum PSF on each variable to reduce non-conservatisms. In light of the existence of efficient probabilistic techniques, the non-conservatisms that have been found in the PSF method, coupled with the impracticality of completely removing these non-conservatisms, it is recommended that a full probabilistic assessment should generally be performed.

scholarly journals Digital Image Separation Algorithm Based on Joint PDF of Mixed Images

2015 ◽  
Vol 20 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Mohd Wajid ◽  
Mayank Sharma
Keyword(s):  
Signal To Noise Ratio ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Scatter Plot ◽  
Separation Algorithm ◽  
Image Separation ◽  
Joint Probability Density ◽  
Separation Results ◽  
Probability Density Function Pdf ◽  
Better Than

Abstract In this article, we have presented an algorithm for separating the mixed or fused images. We have considered that the two independent histogram equalized digital images are linearly mixed, and the joint probability density function (PDF) or the scatter plot of the two observed or mixed images is used for separation. The objective and subjective separation results are presented, and observed to be better than the other existing techniques in terms of Peak signal-to-noise ratio (PSNR) and Signal-to-interference ratio (SIR).

Response Based Design Metocean Conditions for an FPSO by the Extremum Search Within Joint Metocean PDF

2015 ◽  
Author(s):  
Y. Drobyshevski ◽  
H. Wadhwa ◽  
J. R. Whelan
Keyword(s):  
Joint Probability ◽  
Search Process ◽  
Wave Loads ◽  
Site Specific ◽  
Response Sensitivity ◽  
Floating System ◽  
Extreme Responses ◽  
Conditional Maximum ◽  
Joint Probability Density ◽  
Probability Density Function Pdf

The response based analysis of a floating system aims at the prediction of extreme N-year return period responses by analysing their statistics in site-specific metocean conditions. After the extreme responses have been determined the inverse problem is solved: the most probable design metocean conditions (DMC) should be determined which cause these responses. The most probable DMC associated with a given response can be determined by using the joint probability density function (PDF) of metocean parameters. Mathematically, the most probable DMC is equivalent to a “design point”, at which the joint metocean PDF has a conditional maximum. This paper presents a method for finding the most probable DMC by the extremum search. Several numerical examples are presented to demonstrate finding the most probable DMCs for typical responses of an FPSO in North Sea environment, including global motions, wave loads, turret motions and relative wave motions. The most probable DMCs are found by the search process implemented in the space of physical metocean variables, which allows for easy interpretation of the results and exploring the response sensitivity to variability of metocean parameters.

TEST PLAN ALLOCATION TO MINIMIZE SYSTEM RELIABILITY ESTIMATION VARIABILITY

2004 ◽  
Vol 11 (03) ◽  
pp. 257-272 ◽  
Author(s):  
JOSE E. RAMIREZ-MARQUEZ ◽  
DAVID W. COIT ◽  
TONGDAN JIN
Keyword(s):  
System Reliability ◽  
Reliability Estimation ◽  
System Structure ◽  
System Level ◽  
Reliability Estimate ◽  
Component Level ◽  
Component Reliability ◽  
Allocation Process ◽  
Reliability Estimates ◽  
Estimate Variance

A new methodology is presented to allocate testing units to the different components within a system when the system configuration is fixed and there are budgetary constraints limiting the amount of testing. The objective is to allocate additional testing units so that the variance of the system reliability estimate, at the conclusion of testing, will be minimized. Testing at the component-level decreases the variance of the component reliability estimate, which then decreases the system reliability estimate variance. The difficulty is to decide which components to test given the system-level implications of component reliability estimation. The results are enlightening because the components that most directly affect the system reliability estimation variance are often not those components with the highest initial uncertainty. The approach presented here can be applied to any system structure that can be decomposed into a series-parallel or parallel-series system with independent component reliability estimates. It is demonstrated using a series-parallel system as an example. The planned testing is to be allocated and conducted iteratively in distinct sequential testing runs so that the component and system reliability estimates improve as the overall testing progresses. For each run, a nonlinear programming problem must be solved based on the results of all previous runs. The testing allocation process is demonstrated on two examples.

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.

Time-Dependent System Reliability Analysis With Second-Order Reliability Method

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Hao Wu ◽  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failures. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method using the envelope method and second-order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the second-order component reliability method with an improve envelope approach, which produces a component reliability index. The covariance between component responses is estimated with the first-order approximations, which are available from the second-order approximations of the component reliability analysis. Then, the joint distribution of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

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