The reliability estimation of pipeline using FORM, SORM and Monte Carlo Simulation with FAD

2006 ◽  
Vol 20 (12) ◽  
pp. 2124-2135 ◽  
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Estimation

System Reliability Estimation for a Manufacturing Network with Joint Buffers

2018 ◽  
Vol 25 (04) ◽  
pp. 1850020 ◽  
Author(s):  
Ping-Chen Chang ◽  
Chia-Chun Wu ◽  
Chin-Tan Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
System Reliability ◽  
Manufacturing System ◽  
Reliability Estimation ◽  
Experimental Result ◽  
Production Lines ◽  
Work In Process ◽  
Process Output ◽  
Manufacturing Network

This paper develops a Monte Carlo Simulation (MCS) approach to estimate the performance of a multistate manufacturing network (MMN) with joint buffers. In the MMN, products are allowed to be produced by two production lines with the same function to satisfy demand. A performance index, system reliability, is applied to estimate the probability that all workstations provide sufficient capacity to satisfy a specified demand and buffers possess adequate storage. The joint buffers with finite storage are considered in the MMN. That is, extra work-in-process output from different production lines can be stored in the same buffer. An MCS algorithm is proposed to generate the capacity state and to check the storage usage of buffers to evaluate whether the demand can be satisfied or not. System reliability of the MMN is estimated through this MCS algorithm. Besides, performability for demand pairs assigned to production lines can be obtained. A practical example of touch panel manufacturing system is used to demonstrate the applicability of the MCS approach. Experimental result shows that system reliability is overestimated when buffer storage is assumed to be infinite. Moreover, joint buffer for an MMN is more reliable than buffers are installed separately in different production lines.

scholarly journals RELIABILITY ESTIMATION FOR MAINTENANCE BY SEQUENTIAL MONTE CARLO SIMULATION

2009 ◽  
Vol 65 (3) ◽  
pp. 758-775 ◽  
Author(s):  
Ikumasa YOSHIDA ◽  
Mitsuyoshi AKIYAMA ◽  
Shuichi SUZUKI ◽  
Masato YAMAGAMI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Reliability Estimation

Reliability Estimation of Buried Pipeline Using FORM, SORM and Monte Carlo Simulation

2006 ◽  
Vol 326-328 ◽  
pp. 597-600 ◽  
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Exposure Time ◽  
Failure Probability ◽  
Reliability Estimation ◽  
Buried Pipeline ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method ◽  
The Difference

In this paper, the failure probability is estimated by using the FORM (first order reliability method), the SORM (second order reliability method) and the Monte Carlo simulation to evaluate the reliability of the corroded pipeline. It is found that the FORM technique is more effective in estimating the failure probability than the SORM technique for B31G and MB31G models with three different corrosion models. Furthermore, it is noted that the difference between the results of the FORM, the SORM and the Monte Carlo simulation decreases with the increase of the exposure time.

Improving the first-order structural reliability estimation by Monte Carlo simulation

2017 ◽  
Vol 170 (7) ◽  
pp. 532-540 ◽  
Author(s):  
Mohsen Ali Shayanfar ◽  
Mohammad Ali Barkhordari ◽  
Moien Barkhori ◽  
Mohammad Barkhori
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Reliability Estimation ◽  
First Order

Systematic Vibration Reliability Analysis of Random Parametered Gear Sets With Analytic and Multi-Crossing Monte Carlo Simulation Methods

2017 ◽  
Author(s):  
Ma Yupeng ◽  
Zhang Jianguo ◽  
Qiu Jiwei
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Vibration Response ◽  
Reliability Estimation ◽  
Driven Systems ◽  
Vibration Responses ◽  
Vibration Reliability ◽  
Definition Of ◽  
Nonstationary Stochastic Processes

Vibration reliability analysis of gear sets considering various kinds of nonlinear random factors is essential for the safety of gear driven systems. In this paper, a rational definition of gear sets vibration reliability was presented at first by taking all kinds of vibration responses including displacement, velocity and acceleration into account uniformly by treating them as a series system with statistically independent components. According to the given definition, a systematic analyzing scheme for the vibration reliability of gear sets was proposed. Vibration reliability estimated via the analyzing scheme would make it conservative but more safely in design of gear driven systems. Subsequently, both analytic and numerical methods for gear sets vibration response reliability estimation were carried out based on the proposed analyzing scheme. The analytic method is suitable for the situations that the vibration responses of gears sets under random circumstances are stationary stochastic responses. While, the numerical method named Multi-crossing Monte Carlo Simulation (MULCMCS) can well solve the reliability estimating problems even when the vibration responses of gear sets are nonstationary stochastic processes. Finally, for illustration, a numerical case of analyzing the vibration response reliability of a single degree-of-freedom (DOF) gear set was given to demonstrate the effectiveness of the MULCMCS method.

On Reliability Estimation for the Exponential Distribution Based on Monte Carlo Simulation

2018 ◽  
pp. 428
Author(s):  
Abbas Najim Salman ◽  
Taha Anwar Taha
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Reliability Function ◽  
Reliability Estimation ◽  
Estimation Methods ◽  
Shrinkage Methods ◽  
Shrinkage Method ◽  
Two Parameters

        This Research deals with estimation the reliability function for two-parameters Exponential distribution, using different estimation methods ; Maximum likelihood, Median-First Order Statistics, Ridge Regression, Modified Thompson-Type Shrinkage and Single Stage Shrinkage methods. Comparisons among the estimators were made using Monte Carlo Simulation based on statistical indicter mean squared error (MSE) conclude that the shrinkage method perform better than the other methods

Reliability of Buried Pipeline Using FORM and Monte Carlo Simulation

2006 ◽  
Vol 321-323 ◽  
pp. 1543-1547 ◽  
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Probability ◽  
Probabilistic Method ◽  
Reliability Estimation ◽  
Buried Pipeline ◽  
Two Phase ◽  
Surface Corrosion ◽  
Reliability Method ◽  
Power Models

The reliability estimation of pipelines is performed with help of the probabilistic method which considers the uncertainties included in the load and resistance parameters. The FORM (first order reliability method) is carried out to calculate the failure probability of corroded pipeline for the classified six distribution types of random variables. And the reliability of pipelines is assessed by using the failure probability. Furthermore, the MCS (Monte Carlo Simulation) is used to verify the results of the FORM. It is noted that the failure probabilities of the linear and the power models for surface corrosion are similar and those of the two-phase model for surface corrosion are somewhat different for six cases of two failure pressure models such as ASME B31G and MB31G models. And it is also found that the FORM and the MCS give similar results for varying exposure time.

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