Distributions

A statistical distribution is fully described by its pdf (or probability density function). In the previous sections, we used the definition of the pdf to show how all other functions most commonly used in reliability engineering and life data analysis can be derived, namely the reliability function, failure rate function, mean time function and median life function, etc. All of these can be determined directly from the pdf definition, or f(t).

Different distributions exist, such as the normal, exponential, etc., and each one of them has a predefined form of f(t) These distribution definitions can be found in many references. In fact, entire texts have been dedicated to defining families of statistical distributions. These distributions were formulated by statisticians, mathematicians and engineers to mathematically model or represent certain behavior. For example, the Weibull distribution was formulated by Walloddi Weibull and thus it bears his name. Some distributions tend to better represent life data and are most commonly called lifetime distributions.

One of the simplest and most commonly used distributions (and often erroneously overused due to its simplicity), is the exponential distribution. The pdf of the exponential distribution is mathematically defined as:

In this definition, note that t is our random variable which represents time and the Greek letter λ (lambda) represents what is commonly referred to as the parameter of the distribution. Depending on the value of λ, f(t) will be scaled differently.

For any distribution, the parameter or parameters of the distribution are estimated from the data. For example, the most well-known distribution, the normal (or Gaussian) distribution, is given by:

μ, the mean, and σ, the standard deviation, are its parameters. Both of these parameters are estimated from the data, i.e. the mean and standard deviation of the data. Once these parameters have been estimated, our function f(t) is fully defined and we can obtain any value for f(t) given any value of t.

Given the mathematical representation of a distribution, we can also derive all of the functions needed for life data analysis, which again will depend only on the value of t after the value of the distribution parameter or parameters have been estimated from data. (Note: Do not worry about how these parameters are estimated at this point, as this will be discussed in subsequent sections and appendices. For now, assume that we can assign values to these parameters based on our data.)

For example, we know that the exponential distribution pdf is given by:

Thus, the exponential reliability function can be derived to be:

The exponential failure rate function is:

The exponential Mean-Time-To-Failure (MTTF) is given by:

This exact same methodology can be applied to any distribution given its pdf, with various degrees of difficulty depending on the complexity of f(t).

Parameter Types

Distributions can have any number of parameters. Do note that as the number of parameters increases, so does the amount of data required for a proper fit. In general, most distributions used for reliability and life data analysis, the lifetime distributions, usually are limited to a maximum of three parameters. These three parameters are usually known as the scale parameter, the shape parameter and the location parameter.

Scale Parameter

The scale parameter is the most common type of parameter. All distributions in this reference have a scale parameter. In the case of one-parameter distributions, the sole parameter is the scale parameter. The scale parameter defines where the bulk of the distribution lies, or how stretched out the distribution is. In the case of the normal distribution, the scale parameter is the standard deviation.

Shape Parameter

The shape parameter, as the name implies, helps define the shape of a distribution. Some distributions, such as the exponential or normal, do not have a shape parameter since they have a predefined shape that does not change. In the case of the normal distribution, the shape is always the familiar bell shape. The effect of the shape parameter on a distribution is reflected in the shapes of the pdf, the reliability function and the failure rate function.

Location Parameter

The location parameter is used to shift a distribution in one direction or another. The location parameter, usually denoted as γ, defines the location of the origin of a distribution and can be either positive or negative. In terms of lifetime distributions, the location parameter represents a time shift.

This means that the inclusion of a location parameter for a distribution whose domain is normally [0, ] will change the domain to [γ, ], where γ can be either positive or negative. This can have some profound effects in terms of reliability. For a positive location parameter, this indicates that the reliability for that particular distribution is always 100% up to that point γ. In other words, a failure cannot occur before this time γ. Many engineers feel uncomfortable in saying that a failure will absolutely not happen before any given time. On the other hand, the argument can be made that almost all life distributions have a location parameter, although many of them may be negligibly small. Similarly, many people are uncomfortable with the concept of a negative location parameter, which states that failures theoretically occur before time zero. Realistically, the calculation of a negative location parameter is indicative of quiescent failures (failures that occur before a product is used for the first time) or of problems with the manufacturing, packaging or shipping processes. More attention will be given to the concept of the location parameter in subsequent discussions of the exponential and Weibull distributions, which are the lifetime distributions that most frequently employ the location parameter.