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DESCRIPTION:
Although a primary objective of reliability analysis is to i
mprove product reliability\, there are many possible reasons for collectin
g and analyzing reliability data. Several examples are the following:
\
n\n\n - Assessing product reliability in the field
\n - Predic
ting product warranty costs
\n - Estimate replacement part/spares re
quirements
\n - Assessing the effect of a proposed design change
\n - Demonstrating product reliability to customers or government agenc
ies
\n - Comparing components from multiple suppliers
\n - Com
paring components from different production periods\, operating environmen
ts\, or materials
\n - Improving reliability through the use of labo
ratory experiments
\n
\n\nParticipants will gain awareness of t
he overall methodology for setting reliability targets\, estimating produc
t reliability from test data and/or field data\, and determining whether o
r not reliability targets are achieved. Methods for estimating the reliabi
lity of subsystems and systems are also discussed. Participants will also
learn how to calculate sample sizes for reliability testing and utilize re
liability models to develop forecasts of future failures (e.g. warranty fo
recasts).
\n\n
\nWhy you should attend :
\n\n\n - Un
derstand reliability concepts and unique aspects of reliability data
\
n - Understand underlying probability and statistical concepts for relia
bility analysis
\n - Develop competency in the modeling and analysis
of time-to-failure data
\n - Understand reliability metrics and how
to estimate and report them
\n - Estimate reliability of subsystems
and systems
\n - Determine if reliability specifications are met (a
t specified confidence level) or whether design improvements are required<
/li>\n
- Develop competency in the planning of reliability tests (exclud
ing ALT)
\n - Analyze existing warranty data to predict future retur
ns
\n - Develop awareness of more advanced topics in Reliability
\n
\n\n
\nWho will benefit:
\n\n\n - The target au
dience includes anyone with a vested interest in product quality and relia
bility
\n - Product Engineers
\n - Reliability Engineers
\
n - Design Engineers
\n - Quality Engineers
\n - Quality Ass
urance Managers
\n - Project / Program Managers
\n - Manufactu
ring Personnel
\n
\n\nDay 1 Schedule
\n\n
\nLectur
e 1: \;Reliability Concepts and Reliability Data
\
n\n\n - Reliability in Product and Process Development
\n - Un
ique Characteristics of Reliability Data
\n - Censored Data
\n
ul>\n\nProbability and Statistics Concepts
\n\n
\n - Basic Probability Concepts
\n - Probability Distributions (e.
g. Weibull\, Lognormal\, etc.)
\n
\n\n
\nLecture 2: \;
Probability and Statistics Concepts (cont'\;d)
\n\n
\n - Probability Distribution Functions
\n - CDF and Reliabili
ty Functions
\n - Reliability Metrics: Hazard Rate\, Mean Time to Fa
ilure\, Percentiles
\n - Conditional Reliability
\n - Burn-In
(for Infant Mortality)
\n
\n\n
\nLecture 3: \;
Assessing &\; Selecting Models (Distributions) for Failure Data
\n\n\n - Probability Plotting with and without Censored Data\n
- Identifying the Best Distribution(s)
\n - Criteria for Comp
aring Models
\n
\n\n
\nLecture 4: \;Estimation
of Reliability Characteristics
\n\n\n - Estimation Meth
ods (Maximum Likelihood\, Rank Regression)
\n - Reliability/Weibull
Analysis (and other distributions)
\n - Precision of Estimates/Confi
dence Intervals
\n
\n\nDay 2 Schedule
\n\n
\nLectu
re 1: \;Estimation of Reliability Characteristics (cont'\;d
)
\n\n\n - Handling Multiple Failure Modes
\n - Co
mparing Reliability of Different Groups
\n
\n\n
\nLecture
2: \;Introduction to Reliability of Systems
\n\n\n - Series Systems
\n - Parallel Systems
\n - K-out-of-n
Systems
\n - Complex Systems
\n - Introduction to System Model
ing\, Reliability Allocation
\n
\n\n
\nLecture 3: \;Introduction to Reliability Test Planning
\n\n\n - Test planning regimes
\n - Reliability Estimation Test Plans
\n
- Reliability Demonstration Test Plans
\n - Sample Sizes for Esti
mation and Demonstration Test Plans
\n - Sample Size / Testing Time
Trade-offs
\n
\n\n
\nLecture 4: \;Analysis of
Warranty Data
\n\n\n - Data Setup
\n - Identifying
Models for Failure Data
\n - Forecasting Future Warranty Returns\n
- Non-Homogeneous Production Periods
\n
\n\nSteven W
achs
\nPrincipal Statistician\, Integral Concepts\, Inc
\n&nb
sp\;\nSteven Wachs \;has 25 years of wide-ranging industry e
xperience in both technical and management positions. Steve has worked as
a statistician at Ford Motor Company where he has extensive experience in
the development of statistical models\, reliability analysis\, designed ex
perimentation\, and statistical process control.
\n\nSteve is curren
tly a Principal Statistician at Integral Concepts\, Inc. where he assists
manufacturers in the application of statistical methods to reduce variatio
n and improve quality and productivity. He also possesses expertise in the
application of reliability methods to achieve robust and reliable product
s as well as estimate and reduce warranty.
\nEducation
\nM.
A.\, Applied Statistics\, University of Michigan\, 2002
\nM.B.A\, Kat
z Graduate School of Business\, University of Pittsburgh\, 1992
\nB.S
.\, Mechanical Engineering\, University of Michigan\, 1986 \;
\n<
br />\nPlease contact Marilyn Turner: Phone: +1 929 900 1853  \;Email:
marilyn.turner [a] nyeventslist.com for registrations
\n
\n&nbs
p\;
SUMMARY:Predicting & Improving Product Reliability
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SUMMARY:Predicting & Improving Product Reliability
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