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BRASS vs. Weibayes
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BRASS is the state-of-the art product reliability assessment tool that
helps engineers to develop reliability estimates for products in design
or already in operation. .
This tool enables you to develop these estimates where this previously
seemed impossible because reliability performance data is limited or completely
absent.
BRASS gives you the tools needed to leverage valuable sources of information,
such as data from legacy designs, industry handbooks, vendor-supplied
estimates, and engineering knowledge into your assessments.
Contact us to request an evaluation
copy!
Feature Highlights:
Powerful Reliability Modeling
- Model products using a competing failure mode reliability model, and
account for aging effects

- Transform system and component-level data, as well as engineering
judgments into system and component-level reliability assessments
- Analyze accelerated life test data, with certain and uncertain acceleration
factors
- Analyze warranty data
- Apply adjustments using reliability design impact models and data
discounting rule
- Read more...
Full Representation of Uncertainties
- Specify uncertainties regarding the inputs
Compute
the uncertainty surrounding all estimates
- Select from a variety of options for the specification of prior information
- Read more...
Variety of Presentation Features
- Estimate reliability, failure intensity, and cumulative failure intensity
as a function of time
- View estimation results in tabular and graphical format
- Generate failure mode ranking plots and reliability growth plots
- Copy chart and tabular data to the system clipboard, for use in other
software applications
- Read more...
Bayesian Analysis Procedures
- Truly Bayesian analysis, unlike Weibayes
- Easily combine different kinds of information
- Access state-of-the-art Bayesian analysis without need for scripting
| In summary, BRASS is more
than a typical data analysis tool. Through its reliability adjustment
and discounting techniques, and Bayesian analysis procedures, BRASS
makes it possible to use a variety of partially relevant data and
engineering judgments to arrive at reliability estimates, while always
accounting for uncertainty surrounding those estimates. |
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