BRASS vs. Weibayes

 

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. Download brochure. 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.

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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
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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
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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
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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.