System reliability prediction based on historical data

1990 ◽  
Vol 6 (3) ◽  
pp. 209-218 ◽  
Author(s):  
John S. Usher ◽  
Suraj M. Alexander ◽  
John D. Thompson
Keyword(s):  
System Reliability ◽  
Historical Data ◽  
Reliability Prediction

Reliability Analysis of Factors Affecting Gas Pipeline Operational Condition

2011 ◽  
Vol 135-136 ◽  
pp. 720-724
Author(s):  
Zhi Xia Zhang ◽  
Di Wu
Keyword(s):  
Monte Carlo ◽  
System Reliability ◽  
Structural Integrity ◽  
Historical Data ◽  
Operation Time ◽  
Gas Pipeline ◽  
Reliability Prediction ◽  
Weak Links ◽  
Gas Pipelines ◽  
Factors Affecting

The safety of the pipeline in use is tightly linked with the resident life and belongings. Reliable structural integrity and safety of gas pipelines under various service pressure events including defects should be warily evaluated. The reliability evaluation of gas pipelines is necessary to prevent risk. In this paper, Markov chain Monte Carlo is proposed to analyze the reliability of factors affecting gas pipeline operational condition in consideration of the character of historical data, the calculating processes of reliability prediction are provided in view of performance degradation characteristics of the factors.This paper takes corrosion as important factor affecting pipeline operation for example, calculates the reliability indexes of gas pipeline, studies the relation of failure rate and length/depth of corrosion pit ,operation time,etc. seeks weak links of system, and brings forward concrete and reliable measures to improve system reliability.

System Reliability Analysis With Dependent Component Failures During Early Design Stage—A Feasibility Study

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Yao Cheng ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Reliability Prediction ◽  
Design Stage ◽  
Early Design ◽  
Early Design Stage ◽  
Design Concepts ◽  
Reliability Bounds ◽  
Lack Of Information ◽  
Dependent Component ◽  
Component Failures

It is desirable to predict product reliability accurately in the early design stage, but the lack of information usually leads to the use of independent component failure assumption. This assumption makes the system reliability prediction much easier, but may produce large errors since component failures are usually dependent after the components are put into use within a mechanical system. The bounds of the system reliability can be estimated, but are usually wide. The wide reliability bounds make it difficult to make decisions in evaluating and selecting design concepts, during the early design stage. This work demonstrates the feasibility of considering dependent component failures during the early design stage with a new methodology that makes the system reliability bounds much narrower. The following situation is addressed: the reliability of each component and the distribution of its load are known, but the dependence between component failures is unknown. With a physics-based approach, an optimization model is established so that narrow bounds of the system reliability can be generated. Three examples demonstrate that it is possible to produce narrower system reliability bounds than the traditional reliability bounds, thereby better assisting decision making during the early design stage.

scholarly journals Combinatorial Bayesian Dynamic Linear Models of Bridge Monitored Data and Reliability Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xueping Fan ◽  
Yuefei Liu
Keyword(s):  
Linear Models ◽  
Historical Data ◽  
Polynomial Function ◽  
Reliability Prediction ◽  
Structural Components ◽  
Time Data ◽  
Dynamic Linear Models ◽  
Monitoring Mechanism ◽  
Real Time Data ◽  
Order Polynomial

Considering the uncertainties and randomness of the mass structural health monitored data, the objectives of this paper are to present (a) a procedure for effective incorporation of the monitored data for the reliability prediction of structural components or structures, (b) one transforming method of Bayesian dynamic linear models (BDLMs) based on 1-order polynomial function, (c) model monitoring mechanism used to look for possible abnormal data based on BDLMs, (d) combinatorial Bayesian dynamic linear models based on the multiple BDLMs and their corresponding weights of prediction precision, and (e) an effective way of taking advantage of combinatorial Bayesian dynamic linear models to incorporate the historical data and real-time data in structural time-variant reliability prediction. Finally, a numerical example is provided to illustrate the application and feasibility of the proposed procedures and concepts.

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