Efficient Plant June 2018 - 48

column | seeking reliability

Win with
Weibull

T

HE SUBJECT OF Weibull
analysis often evokes a surprisingly negative response from
members of the plant-engineering
community. Some complain Weibull
Drew D. Troyer CRE, CMRP
is only for design engineering, i.e., not
Contributing Editor
usable in the plant. Others insist that
since nearly all failures are random,
condition monitoring is the only tool
required. Those perceptions have some
merit, but overlook several important
aspects of Weibull's value in plants.
Here, I'm beginning a conversation
Randomizing Effect
Following Run-In Period
aimed at dispelling the myths.
I'll provide more detail in an
upcoming enewsletter article.
Early life
Visit the subscriptoin section at
failures
'Constant' Failure Rate
efficientplantmag.com to subFailure
scribe to our newsletters.
Mode A
Let's start with a very simple
Failure
definition
of Weibull analysis: It's
rate
Failure
Failure
Failure
a tool that helps evaluate the risk
mode B
mode E
Failure
mode D
Failure
profile of operating a system over
mode F
mode C
time. In the plant, we often talk
in terms of Mean Time Between/
To Failure (MTBF/MTTF). Alas,
System
failure
"average" can be a very dangerous
rate
Failure
number to know. Weibull analysis
mode G
enables us to determine whether
Time
the risk of failure is:
Parsing the individual failure modes in a complex
 decreasing over time, which indisystem helps solve the 'random trap.'
cates early-life failures
 constant over time, which indicates
random failures
 increasing over time, which indicates
This column explores, from a management
wearout failures.
perspective, the Five Pillars of Knowledge,
as defined by the Society for Maintenance
The beta (β) shape is the key paramand Reliability Professionals (SMRP), Atlanta
eter that provides the risk-profile infor(smrp.org). Expanded, more technical,
mation. If β is approximately 1.0, the
takes on topics covered here will appear in
failure pattern is random. If β is less than
Efficient Plant's monthly "Reliability Solu1.0, the failure pattern is early life (the
tions" e-newsletters. To receive them, visit
closer to zero, the stronger the effect). If
efficientplantmag.com and select Subscribe
under the Magazine menu item.
β is greater than 1.0, the risk of failure

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increases over time, i.e., the higher the
number the greater the effect. Wouldn't
it be helpful for a plant reliability engineer to know that risk pattern?
But back to the criticisms: Let's first
address the theory of Weibull being
useless, given the randomness of most
plant failures. To be clear: No statistical
technique can predict when an individual machine or component will fail.
Statistics provide information about
populations. Thus, we don't use Weibull
to evaluate the health of individual
machines.
Next, the reason plant failures look
random is because we normally collect
data at the system level. Plant machines
are complex dynamic or static systems,
with multiple components and failure
modes.
When all failure modes are thrown
together, there's a randomizing effect
following the run-in period (see figure).
The system-failure rate, however, is the
cumulative sum of all the various failure
modes combined. When we untangle
those different failure modes with effective reliability-data collection (the subject
of a future article), the picture-and the
utility-of Weibull analysis in a plant
becomes clearer.
My e-newsletter article will illustrate
these concepts quantitatively using four
risk-profile scenarios, each with the same
MTBF/MTTF. For now, go ahead and
give Weibull analysis another look-and
another chance. EP

Based in Tulsa, OK, industry veteran
Drew Troyer is principal with Sigma
Reliability Solutions. Email
Drew.Troyer@sigma-reliability.com.

JUNE 2018
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Table of Contents for the Digital Edition of Efficient Plant June 2018
