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.

scholarly journals Analyzing the Improved Software Reliability Based on the Markov Model by Considering Error propagation

2018 ◽  
Vol 7 (2.32) ◽  
pp. 91
Author(s):  
Dr S. Srinivasa Rao ◽  
D Sowjanya ◽  
CH Dileep Chowdary ◽  
M Harika
Keyword(s):  
Markov Model ◽  
Reliability Analysis ◽  
Software Reliability ◽  
Error Propagation ◽  
High Reliability ◽  
Transformation Techniques ◽  
Model Driven ◽  
Model Based ◽  
Estimate Reliability ◽  
Analysis Design

In the software reliability analysis we proposed an approach, which is named as Model Driven Development method. This is a modelling and model transformation techniques. The Markov model used in reliability fields is modified to adapt to error propagation behaviors of components. The Markov model has been used for results of reliability analysis. Markov model which means that that future or upcoming states depend only on the present state not on the events that occurred before it to ensure high reliability of this software is to estimate reliability accurately in the developing phase. Then a study on the transformation between model based on Architecture & Analysis Design Language (AADL) and Markov model has been done. By considering all these a model based software reliability analysis approach is proposed. 

A Novelty Model for Reliability Assessment of Complex System

2014 ◽  
Vol 571-572 ◽  
pp. 118-123
Author(s):  
Zong Run Yin ◽  
Dong Su ◽  
Jun Shan Li
Keyword(s):  
Complex System ◽  
Information Fusion ◽  
Bayesian Method ◽  
Reliability Assessment ◽  
Reliability Model ◽  
Source Information ◽  
Component Reliability ◽  
Reliability Parameters ◽  
System A ◽  
Go Methodology

Aiming at the difficulty in reliability assessment of complex system. A novelty model based on Bayesian method and GO methodology is proposed. Bayesian method is adopted for multi-source information fusion to build the component reliability model, and then GO methodology is utilized to integrate the component reliability parameters and form the reliability model of the system. At last, an instance of reliability assessment for complex electronic equipment is given to show the effectiveness of the model. Result shows that, this method take advantage of Bayesian method and GO methodology, it provide useful reference for relative applications.

scholarly journals Electrical insulation components reliability assessment and practical Bayesian estimation under a Log-Logistic model

2018 ◽  
Vol 7 (3) ◽  
pp. 1072 ◽  
Author(s):  
E Chiodo ◽  
L P. Di Noia ◽  
F Mottola
Keyword(s):  
Logistic Model ◽  
Reliability Assessment ◽  
Model Identification ◽  
Reliability Function ◽  
Electrical Insulation ◽  
Reliability Model ◽  
Reliability Models ◽  
Inverse Gamma ◽  
Past Data ◽  
Strength Models

This paper deals with the “physical reliability models” assessment and estimation for electrical insulation components. It is well known that the reliability model identification and estimation of most of the modern power system components, such as insulation components, may be better achieved, instead that using limited lifetime data, by the knowledge of the degradation mechanisms. Such mechanisms, which are responsible for component aging and failure, are indeed well established in the field of electrical insulation: this is also the case of the so called “Stress-Strength” models. In particular, the “Log-logistic” model, deduced by a suitable Weibull stress-strength probabilistic model, has found valid applications to the reliability assessment of the insulation components. In the framework of the estimation of such reliability model, a new Bayesian approach, based upon the “Odds Ratio” of the Log-logistic model is developed in this paper, based upon the properties that such information, being proportional to the reliability function, is available to the engineer on the basis of past data; moreover, being proportional to the Weibull scale parameter, allows to exploit known features of its conjugate prior Inverse Gamma distribution. Numerical examples and the results of extensive Monte Carlo simulations demonstrate the feasibility and efficiency of the proposed procedure.  

Traction Substation Electrical Main Connection Reliability Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7293-7299 ◽  
Author(s):  
Xue Cheng Ding ◽  
Zheng You He ◽  
Min Yu
Keyword(s):  
Comparative Analysis ◽  
Reliability Analysis ◽  
Transportation Safety ◽  
Time To Failure ◽  
Reliability Model ◽  
Railway Transportation ◽  
Reliability Models ◽  
Vital Importance ◽  
Traction Substation ◽  
Mean Time

Traction substation reliability is of vital importance for railway transportation safety. To illustrate traction substation reliability, irreparable and reparable reliability models of three types of traction substation Electrical main connection have been established. Based on analysis of simple series and parallel reliability system, system irreparable reliability model is analyzed. The ways of how to get mean time to failure (MTTF) and mean time to first failure (MTTFF) of reparable system are achieved. By comparative analysis of the value of MTTF and MTTFF among three kinds of traction substation main connection reparable and irreparable system, some useful conclusions are found.

scholarly journals Reliability Evaluation Based on Different Distributions of Random Load

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Peng Gao ◽  
Liyang Xie
Keyword(s):  
Dynamic Change ◽  
Strength Degradation ◽  
Longitudinal Distribution ◽  
Reliability Model ◽  
Random Load ◽  
Dynamic Reliability ◽  
Initial Strength ◽  
Reliability Models ◽  
Model Based ◽  
Definition Of

The reliability models of the components under the nonstationary random load are developed in this paper. Through the definition of the distribution of the random load, it can be seen that the conventional load-strength interference model is suitable for the calculation of the static reliability of the components, which does not reflect the dynamic change in the reliability and cannot be used to evaluate the dynamic reliability. Therefore, by developing an approach to converting the nonstationary random load into the random load whose pdf is the same at each moment when the random load applies, the reliability model based on the longitudinal distribution is derived. Moreover, through the definition of the transverse standard load and the transverse standard load coefficient, the reliability model based on the transverse distribution is derived. When the occurrence of the random load follows the Poisson process, the dynamic reliability models considering the strength degradation are derived. These models take the correlation between the random load and the strength into consideration. The result shows that the dispersion of the initial strength and that of the transverse standard load coefficient have great influences on the reliability and the hazard rate of the components.

A Reliability Model Validation Method for Mitigating the Effects of Measurement Uncertainty

2019 ◽  
Author(s):  
Mohammadkazem Sadoughi ◽  
Meng Li ◽  
Joseph Beck ◽  
Chao Hu
Keyword(s):  
Measurement Uncertainty ◽  
Model Validation ◽  
Experimental Measurement ◽  
Limit State ◽  
Reliability Estimation ◽  
State Function ◽  
Reliability Model ◽  
Estimation Model ◽  
Validation Method ◽  
Failure Data

Abstract With the increasing role of numerical modeling in engineering design and development processes, improved techniques are needed for validating computational results against experimental measurements. Most existing validation methods suffer from two main limitations: (i) they are often highly sensitive to the experimental measurement uncertainty, and (ii) extending these methods for reliability model validation requires large quantities of failure data that may be very time-consuming or costly to obtain. In order to overcome the aforementioned limitations, this study proposes an indirect reliability model validation method. First, a new procedure for computing a validation metric is developed based on Richardson extrapolation (RE) to reduce the sensitivity of the metric to the experimental measurement uncertainty. Second, a new validation metric is defined based on the limit state function (LSF) approximation to extend numerical model validation to reliability model validation. The proposed method is illustrated by validating a reliability estimation model for a cantilever beam under a vertical load.

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.

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