There is a meeting for the Reliability Society Chapter coming up.
Please note the change from the typical location:
University of Arizona Engineering building, room 214
March 16 between 6pm and 9pm.
Dinner will be served starting at 5:30pm.
Room will be opened starting at 5pm.
Park in the Second Street Garage.
Directions to the 2nd Street Garage----- Speedway to Mountain.
South on Mountain to 2nd Street.
There is a stoplight there.
You will see the Garage.
Even though it is Spring Break, you will still have to pay for
Directions to the Engineering building
Once parked, leave the garage towards the buildings (south).
You will see a TALL building. That is the Administration Building.
Go west -past the Student Union.
Then you will see the Engineering building.
Bayesian Reliability Presentation
Allan T. Mense, Ph.D., PE, CRE, Principal Fellow, Raytheon Missile
Systems, Tucson, AZ
This presentation will cover Bayesian reliability theory and Monte
Carlo Markov Chain (MCMC) solution methods. In reliability
analysis it makes a great deal of practical sense to use all the
information available, old and/or new, objective or subjective,
when making decisions under uncertainty. This is especially true
when the consequences of the decisions can have a significant
impact, financial or otherwise. Most of us make every day personal
decisions this way, using an intuitive process based on our
experience and subjective judgments. Mainstream statistical
analysis, however, seeks objectivity by generally restricting the
information used in an analysis to that obtained from a current
set of clearly relevant data. Prior knowledge is not used except
to suggest the choice of a particular population model to "fit" to
the data, and this choice is later checked against the data for
reasonableness. Lifetime or repair models using frequentist
methods have one or more unknown parameters, e.g. l in an
exponential failure model R(t) = exp[-lt]. The frequentist
approach considers parameters as fixed but unknown constants to be
estimated using sample data,( e.g. times to failure) taken
randomly from the population of interest. The Bayesian approach
treats these population model parameters as random, not fixed,
quantities. Before looking at the current data, use is made of old
information, or even subjective judgments, to construct a prior
distribution model for these parameters. Current data (via Bayes’
formula) is used to revise the starting assessment, deriving what
is called the posterior distribution model for the population
model parameters. Parameter estimates, along with confidence
intervals (known as credibility intervals), are calculated
directly from the posterior distribution. Credibility intervals
are legitimate probability statements about the unknown
parameters, since these parameters now are considered random, not
fixed. The advantage in using Bayesian statistics is that it
allows prior information (e.g., predictions, test results,
engineering judgment) to be combined with more recent information,
such as test or field data, in order to arrive at a
prediction/assessment of reliability based upon a combination of
all available information.
Dr. Mense is a Principal Engineering Fellow at Raytheon Missile
Systems, Tucson, Arizona. He is a principal advisor to the
director of systems engineering responsible for technical reviews
for all missile systems. He is a co-developer and instructor for
Raytheon-wide courses on Bayesian Reliability, Statistical Design
Methods and Statistical Design of Experiments (DOEs). Prior to
joining Raytheon, Allan was the lead systems engineer for Motorola
in satellite & spacecraft integration, and systems engineer for
largest commercial satellite communications network undertaken in
USA SATCOM (Iridium & Teledesic). He held the positions of Vice
President for Research and Professor of Physics at Florida
Institute of Technology. He operated as chief technology officer
and business development / marketing director for entire
university. Dr. Mense was member of a Science & Technology
Committee in U.S. House of Representatives. He was a member U.S.
Army Science Board and member of the National Academy of
Engineering Study Boards. Dr. Mense is registered as a
Professional Engineer (PE) in the State of Arizona and a Certified
Reliability Engineer (CRE) with ASQ.