| LIFE
DATA ANALYSIS
Wallodi Weibull
invented the Weibull distribution in 1937 while
comparing death rates in different population
groups.
| β<1
(steep fall) |
β=1
(flat) |
β>1
(steep
rise) |
|
|
| Burn
in |
Design
Life |
Wear
out |
|
Figure
No 1
Weibull wear-out life curve
|
It
is now one of the most commonly used methods for
fitting equipment life data and has been used
extensively in the aviation industry. The
essence of Weibull’s work was to discover he
could represent the Bathtub Curve of Figure No.
1 using one mathematical formula. The three
zones of the bathtub curve can be represented
using Weibull parameters beta (shape parameter),
eta (life) and gamma (location).
Understanding the
Weibull shape parameters provides the owners,
users and maintainers of equipment with a tool
to predict the behavior of engineering
components and select effective maintenance
strategies.
β<1
implies infant mortality. Electronic and
mechanical components often have high failure
rates initially. Some components are ‘burnt
in’ prior to use, others require careful
commissioning after installation.
β=1
implies random failures. These failures are
independent of time where an old part is as good
as a new part. Maintenance overhauls are not
appropriate. Condition monitoring and inspection
are strategies used to detect the onset of
failure, and reduce the consequences of failure. |
