Weibull Distribution Characteristics

This article also appears in the Life Data Analysis Reference and Accelerated Life Testing Data Analysis Reference books.

The Weibull distribution is widely used in reliability and life data analysis due to its versatility. Depending on the values of the parameters, the Weibull distribution can be used to model a variety of life behaviors. We will now examine how the values of the shape parameter, β, and the scale parameter, η , affect such distribution characteristics as the shape of the curve, the reliability and the failure rate. Note that in the rest of this section we will assume the most general form of the Weibull distribution, (i.e., the 3-parameter form). The appropriate substitutions to obtain the other forms, such as the 2-parameter form where γ = 0, or the 1-parameter form where β = C = constant, can easily be made.

Effects of the Shape Parameter, β
The Weibull shape parameter, β, is also known as the slope. This is because the value of β is equal to the slope of the regressed line in a probability plot. Different values of the shape parameter can have marked effects on the behavior of the distribution. In fact, some values of the shape parameter will cause the distribution equations to reduce to those of other distributions. For example, when β = 1, the of the 3-parameter Weibull reduces to that of the 2-parameter exponential distribution or:


 * $$ f(t)={\frac{1}{\eta }}e^{-{\frac{t-\gamma }{\eta }}} $$

where $$ \frac{1}{\eta }=\lambda = $$ failure rate. The parameter β is a pure number, (i.e., it is dimensionless). The following figure shows the effect of different values of the shape parameter, β, on the shape of the $$pdf$$. As you can see, the shape can take on a variety of forms based on the value of β.



For $$ 0<\beta \leq 1 $$:
 * As t→0 ( or γ), f(t)→∞.
 * As t→∞, f(t)→0.
 * f(t) decreases monotonically and is convex as it increases beyond the value of γ.
 * The mode is non-existent.

For $$ \beta > 1 \,\!$$:
 * f(t) = 0 at t = 0 ( or γ).
 * f(t) increases as $$ t\rightarrow \tilde{T} $$ (the mode) and decreases thereafter.
 * For β &lt; 2.6 the Weibull $$pdf$$ is positively skewed (has a right tail), for 2.6 &lt; β &lt; 3.7 its coefficient of skewness approaches zero (no tail). Consequently, it may approximate the normal $$pdf$$, and for β &gt; 3.7 it is negatively skewed (left tail). The way the value of β relates to the physical behavior of the items being modeled becomes more apparent when we observe how its different values affect the reliability and failure rate functions. Note that for β = 0.999 , f(0) = ∞ , but for β = 1.001 , f(0) = 0. This abrupt shift is what complicates MLE estimation when β is close to 1.

The Effect of β on the $$cdf$$ and Reliability Function

The above figure shows the effect of the value of β on the $$cdf$$, as manifested in the Weibull probability plot. It is easy to see why this parameter is sometimes referred to as the slope. Note that the models represented by the three lines all have the same value of η. The following figure shows the effects of these varied values of β on the reliability plot, which is a linear analog of the probability plot.




 * R(t) decreases sharply and monotonically for 0 &lt; β &lt; 1 and is convex.
 * For β = 1, R(t) decreases monotonically but less sharply than for 0 &lt; β &lt; 1 and is convex.
 * For β &gt; 1, R(t) decreases as increases. As wear-out sets in, the curve goes through an inflection point and decreases sharply.

The Effect of β on the Weibull Failure Rate

The value of β has a marked effect on the failure rate of the Weibull distribution and inferences can be drawn about a population's failure characteristics just by considering whether the value of β is less than, equal to, or greater than one.



As indicated by above figure, populations with β &lt; 1 exhibit a failure rate that decreases with time, populations with β = 1 have a constant failure rate (consistent with the exponential distribution) and populations with β &gt; 1 have a failure rate that increases with time. All three life stages of the bathtub curve can be modeled with the Weibull distribution and varying values of β. The Weibull failure rate for 0 &lt; β &lt; 1 is unbounded at ( or γ). The failure rate, λ(t), decreases thereafter monotonically and is convex, approaching the value of zero as t→∞ or λ(∞) = 0. This behavior makes it suitable for representing the failure rate of units exhibiting early-type failures, for which the failure rate decreases with age. When encountering such behavior in a manufactured product, it may be indicative of problems in the production process, inadequate burn-in, substandard parts and components, or problems with packaging and shipping. For β = 1, λ(t) yields a constant value of $$ { \frac{1}{\eta }} $$ or:


 * $$ \lambda (t)=\lambda ={\frac{1}{\eta }} $$

This makes it suitable for representing the failure rate of chance-type failures and the useful life period failure rate of units.

For β &gt; 1, λ(t) increases as t increases and becomes suitable for representing the failure rate of units exhibiting wear-out type failures. For 1 &lt; β &lt; 2, the λ(t) curve is concave, consequently the failure rate increases at a decreasing rate as t increases.

For β = 2 there emerges a straight line relationship between λ(t) and t, starting at a value of λ(t) = 0 at t = γ, and increasing thereafter with a slope of $$ { \frac{2}{\eta ^{2}}} $$. Consequently, the failure rate increases at a constant rate as t increases. Furthermore, if η = 1 the slope becomes equal to 2, and when γ = 0, λ(t) becomes a straight line which passes through the origin with a slope of 2. Note that at β = 2, the Weibull distribution equations reduce to that of the Rayleigh distribution.

When β &gt; 2, the λ(t) curve is convex, with its slope increasing as t increases. Consequently, the failure rate increases at an increasing rate as t increases, indicating wearout life.

Effects of the Scale Parameter, η
A change in the scale parameter η has the same effect on the distribution as a change of the abscissa scale. Increasing the value of η while holding β constant has the effect of stretching out the $$pdf$$. Since the area under a $$pdf$$ curve is a constant value of one, the "peak" of the $$pdf$$ curve will also decrease with the increase of η, as indicated in the above figure.


 * If η is increased while β and γ are kept the same, the distribution gets stretched out to the right and its height decreases, while maintaining its shape and location.
 * If η is decreased while β and γ are kept the same, the distribution gets pushed in towards the left (i.e., towards its beginning or towards 0 or γ ), and its height increases.
 * η has the same units as $$t$$, such as hours, miles, cycles, actuations, etc.

Effects of the Location Parameter, γ
The location parameter, γ, as the name implies, locates the distribution along the abscissa. Changing the value of γ has the effect of sliding the distribution and its associated function either to the right (if γ &gt; 0 ) or to the left (if γ &lt; 0 ).''




 * When γ = 0, the distribution starts at $$t=0$$ or at the origin.
 * If γ &gt; 0, the distribution starts at the location γ to the right of the origin.
 * If γ &lt; 0, the distribution starts at the location γ to the left of the origin.
 * γ provides an estimate of the earliest time-to-failure of such units.
 * The life period 0 to + γ is a failure free operating period of such units.
 * The parameter γ may assume all values and provides an estimate of the earliest time a failure may be observed. A negative γ may indicate that failures have occurred prior to the beginning of the test, namely during production, in storage, in transit, during checkout prior to the start of a mission, or prior to actual use.
 * γ has the same units as $$t$$, such as hours, miles, cycles, actuations, etc.