RGA Product Features
ReliaSoft’s RGA offers all of the major reliability growth models, plus advanced analysis methods that are not available anywhere else.
Traditional Reliability Growth Analysis and Results
RGA supports all of the traditional reliability growth analysis models:
- Crow-AMSAA (NHPP)
- Standard and Modified Gompertz
The software provides options for time-to-failure (continuous), discrete (success/failure) and reliability data from a variety of different types of developmental reliability growth tests. The available model(s) depend on the type of data.
Reliability Growth Projections, Planning and Management
Available only in RGA, the software supports several innovative approaches that expand upon traditional reliability growth methods in ways that better represent real-world testing practices and practical applications.
- The Crow Extended model allows you to classify failure modes based on whether and when they will be fixed. This allows you to make reliability growth projections and evaluate the reliability growth management strategy.
- The Growth Planning Folio helps you to create a multi-phase reliability growth testing plan. In addition, you can use the Crow Extended – Continuous Evaluation model to analyze data from multiple test phases and create a Multi-Phase Plot to compare your test results against the plan. This will help to determine if it is necessary to make adjustments in subsequent test phases in order to meet your reliability goals.
- The Discrete Reliability Growth Planning folio allows you to develop the overall strategy for one-shot devices.
- The Mission Profile Folio helps you create a balanced operational test plan and track the actual testing against the plan to make sure the data will be suitable for reliability growth analysis.
Make the Most of a Limited Data Set for Fielded Repairable System Analysis
RGA also provides opportunities for fielded repairable system analysis. This includes a Reliability Test Design tool for repairable systems (based on the non-homogeneous Poisson process) and data sheets that have been specially designed for the analysis of fielded system data. Depending on the characteristics of your data set, you may be able to:
- Analyze the failure times for fielded repairable systems in order to understand the reliability over time and calculate metrics of interest (such as optimum overhaul time or expected number of failures) without the detailed data sets that would normally be required.
- Evaluate the reliability improvement that you can expect from rolling out fixes for a fleet of units operating in the field.
- Use grouped (interval) data analysis to evaluate fleet warranty data in order to estimate future returns.