Introduction to Reliability Engineering
When something or someone is reliable
they are dependable and trustworthy. They can be counted on to get
the job done, and they do not fail in the task they are expected to
do, Reliability Engineering is so named because it is concerned with
the dependability and expected performance of products and systems.
Engineers in every field apply the sciences of physics and
mathematics to find suitable solutions to problems or to make
improvements. What distinguishes reliability engineering is that it
has a time-based concept of quality. It is concerned not only with
whether or not a product does fail, but also the time when the
failure of that product occurs.
A formal definition would be that Reliability Engineering is a field
that deals with the ability of a system or a component to perform its
required functions under stated conditions for a specified period of
time. Predicting if a product will or will not fail and when this
failure might happen involves uncertainty. These are questions that
reliability engineers answer with mathematical probabilities and
statistical methods.
The main objectives of reliability
engineering are to prevent or reduce failures, to identify and
correct the causes when failures do occur, to find ways of coping
with failures if their causes have not been corrected yet, and to
estimate the reliability of new designs and analyze reliability data.
These tasks are managed by a reliability engineer, who will have an
accredited engineering degree and have additional
reliability-specific education and training. Reliability engineering
as a separate discipline originated in the United States during the
1950's and has since continued to grow in its effectiveness and
influence.
"What Makes Reliability
Analysis Different?
* The central role played by the
Weibull and the lognormal distributions, rather than the normal
distribution, to represent the statistical distribution of product
lifetime.
* The interest in the distribution
tails (for example, determining the time at which 1% of the product
will fail), rather than mean life and its standard deviation.
* The prevalence of censored data (for
example, on unfailed units for which the current running times, but
not the eventual failure times, of some units are known).
* The use of accelerated testing to
help measure and improve reliability.
* The frequent need to evaluate the
reliability of systems made up of often replaceable parts, each with
their own lifetime distributions.
* The frequent need to extrapolate
beyond the range of the data (for example, extrapolation in time to
predict three-year warranty returns from one-year data or
extrapolation in temperature to assess device reliability from a
high-temperature accelerated test)."
For more great references and materials
see ASQ's web site at http://www.asq.org/.
Hahn, Gerald J.,
Doganaksoy, Necip, "Statistics, By the Numbers." Six
Sigma Forum Magazine Aug. 2009: 28