Advanced Reliability Modeling
BRASS allows reliability predictions to be generated for components as well as systems, by modeling the reliability behavior in terms of multiple failure modes that can be analyzed individually as well as jointly. Weibull time-to-failure distribution models allow aging effects to be considered. Furthermore, the acceleration of each failure mode by user-specified test conditions can be specified in certain and uncertain terms.

Reliability Data Collection
Different types of data, such as life test data and warranty data can be combined in a single analysis, such that they can be jointly analyzed. A flexible import module allows data sets to be read from Microsoft Excel® worksheets. Data sets can be tailored to correspond to forms in use by your organization.

Reliability adjustment models allow differences in product design and operating conditions to be accounted for in a systematic and repeatable manner. For example, the estimated range of increase or decrease of failure rates between product designs can be specified, allowing the pool of available data to be widened to include that of similar products, while accounting for potential biases and avoiding overly confident reliability estimates.

Statistical discounting rules are available to discount observed failures, when design modifications are implemented to resolve the related design issues.

Bayesian Weibull Analysis
BRASS employs fully Bayesian algorithms to analyze the data and engineering judgments. All generated reliability estimates, such as the reliability, failure intensity, and cumulative failure intensity, therefore include the corresponding uncertainty bounds, allowing for a realistic appreciation of the results.

Powerful prior specification options allows the user to handle both situations where no expectations are available, os well as situations in which prior knowledge regarding failure rates exist.

BRASS however largely hides mathematical and computational complexities from the user, and presents the relevant estimates and associated uncertainties in graphical and tabular formats that are easy to interpret.

Flexible Presentation of Results
The estimated reliability measures can be presented using a variety of graphical and tabular formats. The time-based estimates for different products and failure modes can be combined in reliability measure plots, allowing their trends over time to be compared. Ranking charts provide a more condensed side-by-side representation of the impact of all failure modes on the overall reliability.

Furthermore, analysis results can easily be incorporated into growth charts, allowing the reliability to be tracked over the course of a product development cycle. All results can be copied to the system clipboard, and thus simply transferred to spreadsheet or word processing applications.