An Introduction to the Basics of Reliability and Risk by Enrico Zio

By Enrico Zio

The need of craftsmanship for tackling the advanced and multidisciplinary safety issues and hazard has slowly permeated into all engineering functions in order that chance research and administration has won a suitable position, either as a device in help of plant layout and as an crucial ability for emergency making plans in unintentional occasions. This includes the purchase of applicable reliability modeling and probability research instruments to enrich the fundamental and particular engineering wisdom for the technological zone of program. geared toward delivering an natural view of the topic, this booklet presents an advent to the valuable options and matters relating to the security of contemporary business actions. It additionally illustrates the classical strategies for reliability research and probability overview utilized in present perform.

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Additional resources for An Introduction to the Basics of Reliability and Risk Analysis (Series on Quality, Reliability and Engineering Statistics) (Series on Quality, Reliability and Engineering Statistics)

Example text

In other words, general mathematical models of random behaviours can be built which apply to different physical phenomena which behave similarly. 1 Probability functions Cumulative distribution function The cumulative distributionfunction (cdf) F, ( x ) of the random variable X gives the probability of the event {X I x } for any numerical value x . n . n , is termedprobability mass function (pmf) and gives a more detailed information on the behaviour of the random variable. 5 Random variables 41 Probability density function (continuous random variables) Consider a random variable X which can take on continuous values x in '$3, with cdfF,(x).

The former (0: ) , called variance and often indicated also as Var[X], gives a measure of the spread of the distribution around the mean: the larger it is, the more the distribution is spread out over % around the mean; the smaller it is, the more the distribution is peaked on the mean a : ) is called kurtosis and gives a measure of value. e. e. values larger than the mean are more dispersed in a large tail). 26) P X where ox is the square root of the variance and is called standard deviation (often also indicated as S t d [ A ) .

1 Probability functions Cumulative distribution function The cumulative distributionfunction (cdf) F, ( x ) of the random variable X gives the probability of the event {X I x } for any numerical value x . n . n , is termedprobability mass function (pmf) and gives a more detailed information on the behaviour of the random variable. 5 Random variables 41 Probability density function (continuous random variables) Consider a random variable X which can take on continuous values x in '$3, with cdfF,(x).

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