Reliability Growth Data Folio
For reliability growth data analysis only.
The Reliability Growth data folio (RGA) is the most commonly
used folio for reliability growth analysis. Depending on the data
type selected, the folio can be used for several different types
of analysis.
Note: Reliability Growth data folios are available only if supported by your license. For more information, contact us.
- Creating a growth data folio.
- Data
entry and management, including:
- Selecting the appropriate data type
- Normal and advanced systems views for analyzing data from multiple systems
- Altering the data type
- Transferring Reliability Growthdata to a life data folio
- Using the growth folio control panel
- Performing calculations via the Quick Calculation Pad (QCP)
- Generating Reliability Growth Plots with the Plot Utility.
- Editing, copying and/or printing analysis results via the Results window
- Publishing models from analysis results
- Updating spreadsheets and reports from prior versions
Tools
- Evaluating how the model fits the data by using statistical tests
- Using the interval goodness-of-fit test to group the failure times into intervals
Analysis Tools Shared with Life Data Folios
- Extracting subsets of data via the batch auto run process
- Performing "what-if" analysis by altering parameters
- Performing calculations with no data entered
- Creating random data for an analysis
- Using the Auto Group Data feature to organize data into groups
For more focused information about performing a particular type of analysis, you can go directly to the topic of interest, which provides information about the applicable data types and models, analysis assumptions, available results, application examples, etc.
- Traditional Reliability Growth Analysis: This type of analysis is used for data from in-house reliability growth testing that was conducted during the developmental stages for a product. The analysis assumes that all fixes (i.e., permanent design improvements) are applied immediately after failure and before testing resumes, and that a reliability growth model can be fitted to the data in order to track how the reliability changes over time. The metrics of interest may include the reliability, MTBF, failure intensity, expected number of failures for a given time, and the amount of testing that will be required to demonstrate a specified reliability. Depending on the data type, the following statistical models can be used in the analysis: Crow-AMSAA (NHPP), Standard Gompertz, Modified Gompertz, Lloyd-Lipow, Duane or Logistic.
- Reliability Growth
Projections, Planning and Management: This type of
analysis focuses on how the reliability growth management
strategy (i.e., which modes are fixed and when) affects the
reliability growth potential of the product. Instead of assuming
that all fixes are applied immediately after failure and before
the observational period resumes, you can use classifications
to account for different fix strategies employed for different
failure modes.
- The Crow Extended model is designed for a single test phase, and it classifies failure modes as A = no fix, BC = fixed at some time during the test or BD = fix delayed until after the end of the test.
- The Crow Extended – Continuous Evaluation model can be used for single or multiple test phases, and it classifies failure modes as A = no fix, BC = fixed immediately before testing resumes or BD = fixed at some point after testing resumes (i.e., later in the same test phase, between test phases, in a subsequent test phase or after all test phases).
- The growth planning folio and multi-phase plot can be used to develop an effective reliability growth test plan, and visualize the test results across multiple phases.
- Repairable Systems Analysis: This type of analysis is used for data from repairable systems operating in the field under typical customer usage conditions. Such data might be obtained from a warranty system, repair depot, operational testing, etc. You can use the power law or Crow-AMSAA (NHPP) models for repairable system analysis based on the assumption of minimal repair (i.e., the system is "as bad as old" after each repair) to calculate metrics such as the expected number of failures, rate of wearout or the optimum time to replace or overhaul a system to minimize life cycle costs.
