Reliability growth modeling and planning
Reliability Growth, formerly known as RGA software, is an advanced module application available in ReliaSoft Weibull++ that allows you to apply reliability growth models to analyze data from both developmental testing and fielded repairable systems. During the development phase, it allows you to quantify and track the system’s reliability growth across multiple test phases, while also providing advanced methods for reliability growth projections, planning and management. For systems operating in the field, you can calculate optimum overhaul times and other results without the detailed data sets.
Improve reliability analysis with Reliability Growth module
Predict failures before they occur
Forecast failures with reliability growth analysis and make reliability growth projections to evaluate the reliability growth management strategy.
Monitor reliability improvements over time
Determine the feasibility of achieving a target MTBF with a given management testing strategy. Evaluate the reliability improvement from rolling out fixes for a fleet of units operating in the field.
Calculate optimum overhaul times
Easily determine the amount of testing required to demonstrate a specified MTBF, failure intensity, or reliability without the detailed data sets that would normally be required for repairable system analysis.
Quantify reliability growth across multiple test phases
Effortlessly perform reliability growth projections, reliability growth program planning and multi-phase reliability growth analysis.
Design test plans for repairable systems
Determine the test time required per system in order to demonstrate a specified reliability goal.
Experiment with sample sizes with simulation tools
Automatically analyze and plot results from a large number of data sets that have been created via simulation. Perform a wide variety of reliability tasks with integrated simulation tools.
Integrate repairable systems analysis with other processes
The Reliability Growth module facilitates the analysis of repairable systems data using the Crow (AMSAA) model. It allows you to get an overview of the system without having the large data requirements that would normally be required for system reliability analysis.
With the Reliability Growth module, you can track the progress of the system during development phase and then use ReliaSoft BlockSim in accordance with the already known results to gain more detailed information.
Key Features
System reliability, availability, and maintainability analysis
When you have data from developmental testing in which the systems were operated continuously until failure, you can use the Crow-AMSAA (NHPP) or Duane models. The module provides a choice of data types for individual or grouped failure times, and for combining data from multiple identical systems. This can include situations, where all systems operate concurrently, you have recorded the exact operating times for both the failed and non-failed systems or you have recorded the calendar date for each failure so you can estimate the operating times of the non-failed systems based on the average daily usage rate for the relevant time period.
With the Crow-AMSAA (NHPP) model, there are additional analysis options for certain situations, such as Gap analysis (if you believe that some portion of the data is erroneous or missing) or change of slope (if a major change in the system design or operational environment has caused a significant change in the failure intensity observed during testing).
When you have data from one-shot (pass/fail) reliability growth tests (and depending on the data type), the module supports mixed data models that can be used with Crow Extended and Crow Extended-Continuous Evaluation models. For discrete data, there is a choice of data types that can handle tests in which a single trial is performed for each design configuration, multiple trials per configuration, or a combination of both. The module also supports Failure Discounting, if you have recorded the specific failure modes from sequential one-shot tests.
When you simply wish to analyze the calculated reliability values for different times/stages within developmental testing, you can use the Standard Gompertz, Modified Gompertz, Lloyd-Lipow or Logistic models.
- 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.
- An MIL-HDBK-189 planning model is available in the Continuous Growth Planning folio.
- Optimum overhaul time for a given repair cost and overhaul cost
- Conditional reliability, MTBF or failure intensity for a given time
- Expected number of failures for a given time
- Time for a given conditional reliability, MTBF or failure intensity
- Expected fleet failures calculation for the number of failures that are expected to occur for all systems by a specified time
- Designing reliability growth tests.
- Obtaining simulation-based confidence bounds.
- Experimenting with the influences of sample sizes and data types on analysis methods.
- Evaluating the impact of allocated test time.
Training Courses
Find training for life data analysis, accelerated life testing, and reliability growth with guided usage of Weibull++ software.
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Software Videos
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Case Studies
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