Model-Based Reliability Analysis With Both Model Uncertainty and Parameter Uncertainty

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Zhimin Xi
Keyword(s):  
Reliability Analysis ◽  
Epistemic Uncertainty ◽  
Uncertainty Modeling ◽  
Element Model ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Model Bias ◽  
Estimation Errors ◽  
Reliability Modeling ◽  
Model Based

Model-based reliability analysis may not be practically useful if reliability estimation contains uncontrollable errors. This paper addresses potential reliability estimation errors from model bias together with model parameters. Given three representative scenarios, reliability analysis strategies with representative methods are proposed. The pros and cons of these strategies are discussed and demonstrated using a tank storage problem based on the finite element model with different fidelity levels. It is found in this paper that the confidence-based reliability analysis considering epistemic uncertainty modeling for both model bias and model parameters can make reliability estimation errors controllable with less conservativeness compared to the direct reliability modeling using the Bayesian approach.

Reliability Analysis With Model Uncertainty Coupling With Parameter and Experiment Uncertainties: A Case Studyof 2014 Verification and Validation Challenge Problem

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhimin Xi ◽  
Ren-Jye Yang
Keyword(s):  
Reliability Analysis ◽  
Model Uncertainty ◽  
National Laboratory ◽  
Sandia National Laboratory ◽  
Uncertainty Modeling ◽  
Verification And Validation ◽  
Model Parameters ◽  
Model Bias ◽  
Eigenvector Dimension Reduction ◽  
Validation Strategy

A validation strategy with copula-based bias approximation approach is proposed to address the 2014 Verification and Validation (V & V) challenge problem developed by the Sandia National Laboratory. The proposed work further incorporates model uncertainty into reliability analysis. Specific issues have been addressed including: (i) uncertainty modeling of model parameters using the Bayesian approach, (ii) uncertainty quantification (UQ) of model outputs using the eigenvector dimension reduction (EDR) method, (iii) model bias calibration with the U-pooling metric, (iv) model bias approximation using the copula-based approach, and (v) reliability analysis considering the model uncertainty. The proposed work is well demonstrated in the challenge problem.

A Methodology for Model-Based Reliability Estimation

2014 ◽  
pp. 450-472
Author(s):  
Mohd Adham Isa ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Reliability Analysis ◽  
Reliability Assessment ◽  
Reliability Estimation ◽  
Reliability Model ◽  
Estimation Model ◽  
Domain Experts ◽  
Reliability Models ◽  
Model Based ◽  
Reliability Parameters ◽  
Estimate Reliability

In recent years, reliability assessment is an essential process in system quality assessments. However, the best practice of software engineering for reliability analysis is not yet of its matured stage. The existing works are only capable to explicitly apply a small portion of reliability analysis in a standard software development process. In addition, an existing reliability assessment is based on an assumption provided by domain experts. This assumption is often exposed to errors. An effective reliability assessment should be based on reliability requirements that could be quantitatively estimated using metrics. The reliability requirements can be visualized using reliability model. However, existing reliability models are not expressive enough and do not provide consistence-modeling mechanism to allow developers to estimate reliability parameter values. Consequently, the reliability estimation using those parameters is usually oversimplified. With this situation, the inconsistency problem could happen between different estimation stages. In this chapter, a new Model-Based Reliability Estimation (MBRE) methodology is developed. The methodology consists of reliability model and reliability estimation model. The methodology provides a systematic way to estimate system reliability, emphasizing the reliability model for producing reliability parameters which will be used by the reliability estimation model. These models are built upon the timing properties, which is the primary input value for reliability assessment.

A Methodology for Model-Based Reliability Estimation

2017 ◽  
pp. 461-484
Author(s):  
Mohd Adham Isa ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Reliability Analysis ◽  
Reliability Assessment ◽  
Reliability Estimation ◽  
Reliability Model ◽  
Estimation Model ◽  
Domain Experts ◽  
Reliability Models ◽  
Model Based ◽  
Reliability Parameters ◽  
Estimate Reliability

In recent years, reliability assessment is an essential process in system quality assessments. However, the best practice of software engineering for reliability analysis is not yet of its matured stage. The existing works are only capable to explicitly apply a small portion of reliability analysis in a standard software development process. In addition, an existing reliability assessment is based on an assumption provided by domain experts. This assumption is often exposed to errors. An effective reliability assessment should be based on reliability requirements that could be quantitatively estimated using metrics. The reliability requirements can be visualized using reliability model. However, existing reliability models are not expressive enough and do not provide consistence-modeling mechanism to allow developers to estimate reliability parameter values. Consequently, the reliability estimation using those parameters is usually oversimplified. With this situation, the inconsistency problem could happen between different estimation stages. In this chapter, a new Model-Based Reliability Estimation (MBRE) methodology is developed. The methodology consists of reliability model and reliability estimation model. The methodology provides a systematic way to estimate system reliability, emphasizing the reliability model for producing reliability parameters which will be used by the reliability estimation model. These models are built upon the timing properties, which is the primary input value for reliability assessment.

STRUCTURAL RELIABILITY ANALYSIS BASED ON P-BOXES

2020 ◽  
Vol 92 (6) ◽  
pp. 51-58
Author(s):  
S.A. SOLOVYEV ◽  
Keyword(s):  
Probability Distribution ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Epistemic Uncertainty ◽  
Random Variable ◽  
Strength Criterion ◽  
Structural Elements ◽  
Structural Element ◽  
Limit States ◽  
Distribution Shape

The article describes a method for reliability (probability of non-failure) analysis of structural elements based on p-boxes. An algorithm for constructing two p-blocks is shown. First p-box is used in the absence of information about the probability distribution shape of a random variable. Second p-box is used for a certain probability distribution function but with inaccurate (interval) function parameters. The algorithm for reliability analysis is presented on a numerical example of the reliability analysis for a flexural wooden beam by wood strength criterion. The result of the reliability analysis is an interval of the non-failure probability boundaries. Recommendations are given for narrowing the reliability boundaries which can reduce epistemic uncertainty. On the basis of the proposed approach, particular methods for reliability analysis for any structural elements can be developed. Design equations are given for a comprehensive assessment of the structural element reliability as a system taking into account all the criteria of limit states.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

Reliability analysis and state transfer scheduling optimization of degrading load-sharing system equipped with warm standby components

2021 ◽  
pp. 1748006X2110017
Author(s):  
Sheng-Jia Ruan ◽  
Yan-Hui Lin
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
High Reliability ◽  
Quadrature Rule ◽  
Load Sharing ◽  
Measurement Information ◽  
Model Parameters ◽  
Scheduling Optimization ◽  
State Transfer ◽  
Warm Standby

Standby redundancy can meet system safety requirements in industries with high reliability standards. To evaluate reliability of standby systems, failure dependency among components has to be considered especially when systems have load-sharing characteristics. In this paper, a reliability analysis and state transfer scheduling optimization framework is proposed for the load-sharing 1-out-of- N: G system equipped with M warm standby components and subject to continuous degradation process. First, the system reliability function considering multiple dependent components is derived in a recursive way. Then, a Monte Carlo method is developed and the closed Newton-Cotes quadrature rule is invoked for the system reliability quantification. Besides, likelihood functions are constructed based on the measurement information to estimate the model parameters of both active and standby components, whose degradation paths are modeled by the step-wise drifted Wiener processes. Finally, the system state transfer scheduling is optimized by the genetic algorithm to maximize the system reliability at mission time. The proposed methodology and its effectiveness are illustrated through a case study referring to a simplified aircraft hydraulic system.

scholarly journals Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Software Reliability ◽  
Growth Models ◽  
Predictive Performance ◽  
Real Data ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Dual Processes ◽  
Correction Process

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

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