Modeling of reliability

Markov models can deal with a number of complex issues found in the probabilistic modeling of reliability and safety. The models can show system success versus system failure (Figure D-1). 

STOCHASTIC SIMULATION MODEL OF RELIABILITY WITH THE CHOICE OF DECISIVE EVENT... 

The joint general model of reliability and availability of complex technical systems in variable operation conditions linking a semi-markov modeling of the system operation processes with a multi-state approach to system reliability and availability analysis is constructed. Next, the final results of this joint model and a linear programming are used to build the model of complex technical systems reliability optimization. Theoretical results are applied to reliability, risk and availability evaluation and optimization of a port piping oil transportation system. Their other wide applications to port, shipyard and ship transportation systems reliability evaluation and optimization are possible. The results are expected to be the basis to the availability of complex teclmical systems optimization and their operation processes effectiveness and cost optimization as well. 

According to above equations, Bayesian urference model of reliability growth can be established by WinBUGS codes. Depicting as following,... 

A frequent subject of research is a separated element of the rail transport system. An example might be the analyses of technical objects included in the infrastructure. Krenzelok et al. (2010) have developed a 40-state model of reliability and safety of raU-road transit security devices. 

The second layer which consists of a model of reliability and safety of the rail transport system is a layer of improper use. It has been assumed that in this layer there are no states of capability and partial capability in anticipation of the transportation... 

Christer, A.H. (1987). Delay-time model of reliability of equipment subject to inspection monitoring. Journal of the Operational Research Society 38(4) 329-334. 

As an example of the type of functions possible with just one of the functions, Figure 8.12 shows various Weibull probability density plots for a range of Weibull shape parameters. The ability of this rather simple function to take on many different functional forms is one of the reasons it is frequently used for modeling of reliability data. The shape can range from an exponential like decrease with x to approximate a Gaussian for a shape parameter around 4 and an even more sharply... 

Finally some of the eoneepts were brought together in a section on the modeling of reliability data using non-Gaussian statistics. The next chapter will continue with the analysis of data with random errors but will expand flic concepts in this chapter to examples where measured data sets depend on one or more independent variables. 

The primary purpose for expressing experimental data through model equations is to obtain a representation that can be used confidently for systematic interpolations and extrapolations, especially to multicomponent systems. The confidence placed in the calculations depends on the confidence placed in the data and in the model. Therefore, the method of parameter estimation should also provide measures of reliability for the calculated results. This reliability depends on the uncertainties in the parameters, which, with the statistical method of data reduction used here, are estimated from the parameter variance-covariance matrix. This matrix is obtained as a last step in the iterative calculation of the parameters. 

R. E. Barlow and E. Proschan, StatisticalTheory of Reliability andEfe TestingProbability Models, Holt, Rinehart and Winston, Inc., New York, 1975. 

Having previously introduced the key methods to determine the important variables with respect to stress and strength distributions, the most acceptable way to predict mechanical component reliability is by applying SSI theory (Dhillon, 1980). SSI analysis is one of the oldest methods to assess structural reliability, and is the most commonly used method because of its simplicity, ease and economy (Murty and Naikan, 1997 Sundararajan and Witt, 1995). It is a practical engineering tool used for quantitatively predicting the reliability of mechanical components subjected to mechanical loading (Sadlon, 1993) and has been described as a simulative model of failure (Dasgupta and Pecht, 1991). 

Leitch, R. D. 1990 A Statistical Model of Rough Loading. In Proceedings 7th International Conference on Reliability and Maintainability, Brest, France, 8-12. 

Loll, V. 1987 Load-Strength Modelling of Mechanics and Electronics. Quality and Reliability Engineering International, 3, 149-155. 

Schatz, R., Shooman, M. and Shaw, L. 1974 Application of Time Dependent Stress-Strength Models of Non-Electrical and Electrical Systems. In Proceedings Reliability and Maintainability Symposium, 540-547. 

It is impossible to specify a completely foolproof coupling, particularly by manufacturer and model number. Still, in the interest of reliability, the specification should cover the subject of couplings. This requires the use of plant feedback and a lot of research on the part of the specification writer to determine the latest developments in coupling design. Unfortunately, when a vendor furnishes a coupling, it often becomes just another outside purchased item that he may purchase with a minimum specification and strictly on low price. 

RDB, thej eliability database module, creates a user-defined database or retrieves data from the IAEA generic reliability database. The design facilitates RDB development for components, human actions, initiating events and the attributes of components. Component unavailabilities can be calculated from the database of reliability parameters using 10 types of predel mcd leliabiliiv models. 

The Rome Air Development Command (RADC - Rome NY) provides the MIL HDBK 217 series of detailed electronics information. Early reports in this series provided failure rates for electronic components. The development of integrated circuits resulted in the approach of providing parameters for mathematical models of transistors and integrated circuits. RADC also publishes Nonelectronic Parts Reliability Data covering the failure rates of components ranging from batteries to valves. 

Method A Calculating Contaminant and Exhaust Velocities at All Points in the Flow Field Local exhaust hoods are used to remove contaminants at the point of generation before they escape into the workplace air. The efficiency of any local exhaust system is greatly affected by the flow field generated by the exhaust opening. Therefore, accurate modeling of this flow field is essential for reliable predictions. However, solving the airflow field is a formidable task and often must be done numerically. 

Ventilation components with small geometric detail Numerical modeling of diffusers with complex geometry is difficult. Therefore, it is more reliable to measure airflow around such devices at full scale. 

In many industrial halls, conduction inro the ground is a major factor for heat loss. Therefore, an adequate modeling of the floor slab and the underlying, thermally active, soil is very crucial for reliable simulation resuirs. In this case, the soil model in the TRNSYS model was established using results from an additionally performed finite-element program analysis. 

Weibull distribution This distribution has been useful in a variety of reliability applications. The Weibull distribution is described by three parameters, and it can assume many shapes depending upon the values of the parameters. It can be used to model decreasing, increasing, and constant failure rates. 

There are other distributions that can be used in a variety of reliability models. The Poisson, the extreme value, gamma, binomial, and Rayleigh distributions are sometimes used in specialized models. 

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