Data Types and Models for Traditional RGA

For reliability growth data analysis only.

The following information provides a summary of the data types and models that are applicable for traditional reliability growth analysis (i.e., the analysis of developmental testing data with the assumption that fixes are applied immediately after a failure and before testing resumes).

Data Types

You can perform traditional reliability growth analysis with any of the following data types:

• All times-to-failure data types (except for Multiple Systems with Event Codes, which is intended only for growth projections analysis)
• All discrete data types
• Reliability data

Reliability Growth Models

Weibull++ includes six reliability growth models that can be used to track how the reliability changes over time during developmental testing. The models available will depend on the data type. The following list provides links to the Reliability Growth and Repairable System Analysis Reference that discusses in detail the assumptions behind each model.

• Crow-AMSAA (NHPP) -  https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/crow-amsaa_nhpp.html
• Duane - https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/duane_model.html
• Standard and Modified Gompertz - https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/gompertz_models.html
• Lloyd-Lipow - https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/lloyd-lipow.html
• Logistic - https://help.reliasoft.com/reference/reliability_growth_and_repairable_system_analysis/rg_rsa/logistic.html