# Reliability Modeling: k out of n Configutations

A system consisting of n components or subsystems, of which only k need to be functioning for system success, is called a “k-out-of-n” configuration. For such a system, k is less than n. An example of such a system might be an air traffic control system with n displays of which k must operate to meet the system reliability requirement.

# Reliability Modeling: Parallel Configuration

A commonly occurring configuration encountered in reliability mathematical modeling is the parallel configuration as shown in the reliability block diagram below

For this case, for the system to fail, all of the components would have to fail. Continue reading

# Binomial Distribution

The binomial distribution is used for those situations in which there are only two outcomes, such as success or failure, and the probability remains the same for all trials. It is very useful in reliability and quality assurance work. The probability density function (pdf) of the binomial distribution is

# Weibull Distribution

1. The Weibull distribution is particularly useful in reliability work since it is a general distribution which, by adjustment of the distribution parameters, can be made to model a wide range of life distribution characteristics of different classes of engineered items. One of the versions of the failure density function is

# Exponential Distribution

This is probably the most important distribution in reliability work and is used almost exclusively for reliability prediction of electronic equipment. It describes the situation wherein the hazard rate is constant which can be shown to be generated by a Poisson process. This distribution is valuable if properly used. It has the advantages of:

1. single, easily estimated parameter (λ)
2. mathematically very tractable
3. fairly wide applicability
4. is additive  that is, the sum of a number of independent exponentially distributed variables is exponentially distributed.

Some particular applications of this model include:

1. items whose failure rate does not change significantly with age.
2. complex and repairable equipment without excessive amounts of redundancy.
3. equipment for which the early failures or “infant mortalities” have been eliminated by “burning in” the equipment for some reasonable time period.

The failure density function is

for t > 0, where λ is the hazard (failure) rate, and the reliability function is

the mean life (θ) = 1/λ, and, for repairable equipment the MTBF = θ = 1/λ .

# Normal Distribution

There are two principal applications of the normal (or Gaussian) distribution to reliability. One application deals with the analysis of items which exhibit failure due to wear, such as mechanical devices. Frequently the wearout failure distribution is sufficiently close to normal that the use of this distribution for predicting or assessing reliability is valid.

The other application deals with the analysis of manufactured items and their ability to meet specifications. No two parts made to the same specification are exactly alike. The variability of parts leads to a variability in systems composed of those parts. The design must take this part variability into account, otherwise the system may not meet the specification requirement due to the combined effect of part variability. Another aspect of this application is in quality control procedures.

The basis for the use of normal distribution in this application is the central limit theorem which states that the sum of a large number of identically distributed random variables, each with finite mean and variance, is normally distributed. Thus, the variations in value of electronic component parts, for example, due to manufacturing are considered normally distributed.

The failure density function for the normal distribution is
Equ. 1

where
μ = the population mean
σ = the population standard deviation, which is the square root of
the variance.