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ReliaSoft's RGA
Software for Repairable System
and Reliability Growth Analysis

Product Features (Page 1)

[Intro] [1] [2] [3] [4] [5] [6]

Analytical Power
Data Types, Models, Statistical Tests

ReliaSoft's RGA 6 provides unparalleled analytical power for your reliability growth, projections and fielded (repairable) systems analyses with a flexible array of data entry and analysis options depending on the data and analysis types.

Supported Data Types
RGA's data entry spreadsheets support both developmental testing data and fielded systems data. This includes time-to-failure (continuous), success/failure (discrete) and reliability data, entered individually or in groups, cumulative or non-cumulative. You can even define "gaps" for some data types to compensate for missing or questionable data points.

Specifically, you can use RGA to analyze:

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Data from Developmental (Reliability Growth) Testing

Failure Times
- with or without failure mode identification, classification and effectiveness factors (for projections)
- cumulative or non-cumulative
Grouped Failure Times, within specified time intervals
- with or without failure mode identification, classification and effectiveness factors (for projections)
Multiple Systems with Known Equivalent Times (the time of the failure plus the operating times of the other systems under test)
- cumulative or non-cumulative
Multiple Systems with Unknown Equivalent Times, to be combined to form a single "equivalent" system for analysis purposes
- with or without the chronological date of each event
- with or without failure mode identification, classification and effectiveness factors (for projections)
Sequential Success/Failure from one-shot (pass/fail) tests
Sequential Success/Failure with Failure Mode Identification
Grouped Success/Failure per Configurations
- cumulative or non-cumulative
Reliability for different times/stages

Data from Fielded Repairable Systems

Known start, end and failure times for multiple systems, to be combined to form a single "superposition" system for analysis purposes
- with or without the chronological date of each event
- with or without failure mode identification, classification and effectiveness factors (for projections)

Statistical Models
RGA 6 supports all of the major reliability growth analysis models and RGA 6 PRO incorporates statistical models for reliability growth projections and the analysis of fielded (repairable) systems data. This includes new model formulations developed by Dr. Larry Crow exclusively for use in the RGA package. Available models include:

Developmental Testing Data
- Crow Extended for continuous data with the required failure mode classifications and effectiveness factors. New formulation, only available in RGA 6 PRO!
- Duane for continuous and discrete data.

Fielded Systems Data Only available in RGA 6 PRO!

Power Law
Crow Extended

Analysis of success/failure (discrete) data with the Modified Gompertz model. Other data types and models are available.

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Parameter Estimation and Statistical Tests
Depending on the data/analysis type, Maximum Likelihood Estimation (MLE) or Least Squares are used for parameter estimation.

In addition, RGA 6 provides Chi-Squared and Cram'er von Mises (CVM) methods for goodness-of-fit testing (depending on the data type), as well as the Common Beta Hypothesis (CBH) to indicate whether multiple systems should be combined into an "equivalent" or "superposition" system for analysis. The Laplace Trend Test provides an indication of whether the system's reliability is improving, deteriorating or staying the same.