[Please note that the following article — while it has been updated from our newsletter archives — may not reflect the latest software interface and plot graphics, but the original methodology and analysis steps remain applicable.] During the first phases of a product's development, the estimate of the product's final reliability is called the reliability goal. However, the first prototypes produced will almost certainly contain design, manufacturing and/or engineering deficiencies that prevent the product from reaching that goal. In order to identify and correct these deficiencies, prototypes are usually subjected to a rigorous testing program and appropriate corrective actions are implemented to improve the design.

ReliaSoft, an HBM Prenscia brand, promotes best practice reliability engineering methodologies through software, training, and services designed exclusively. When using BlockSim to compute the equation, the software will return the first equation above for the system and the second equation above for the subdiagram. Cad2shape 7.0 serial killer. Even though BlockSim will make these substitutions internally when performing calculations, it does show them in the System Reliability Equation window.

This structured process of finding reliability problems and monitoring the increase of the product's reliability through successive phases is called reliability growth. Until now, the software available for analyzing reliability growth data has been fairly limited. However, ReliaSoft is currently working in cooperation with Dr. Larry Crow, the premier expert in the field of reliability growth, to develop the next generation of reliability growth analysis software, RGA. This article presents a brief overview of the capabilities of RGA and an introduction to some of the results and plots that are available for your reliability growth and related analyses. RGA 6 Overview The RGA software includes a wide variety of features to aid you in your reliability growth analyses so that you not only can obtain the results, but also understand the results. Features include: • Data entry spreadsheets support continuous (time-to-failure), discrete (success/failure) and reliability data.

• Data analysis with the major reliability growth models: Crow (AMSAA), Gompertz, Modified Gompertz, Logistic and Lloyd-Lipow. Maximum Likelihood Estimation (MLE) is used for parameter estimation. • Projections analysis using A, B and C failure mode classifications and the definition of effectiveness factors for use in the Crow (AMSAA) Projection model. This model can be used to estimate the number of unseen failure modes, the maximum achievable reliability and other important metrics. • Repairable systems (overhauls) analysis using the Crow (AMSAA) model and Dr.

Crow's analysis methodology. • Chi-Squared and CVM methods for goodness-of-fit testing (depending on the data type) as well as the Statistical Test for Growth. • Expanded plotting capabilities and automated custom reports created in Microsoft Word and Excel. • The ability to attach any type of file to the analysis. For example, you can attach an Excel file that has the original data or a Word document that contains the report based on the data analysis.

Basic Analysis Results and Plots Although the results and plots that can be generated for your analysis will depend on the type of data that you have collected and the reliability growth model selected for analysis, some basic plots and results can be generated for all analyses. Figures 1 and 2 demonstrate two plots that present reliability growth results over time. Figure 1 presents the expected number of failures and Figure 2 presents the instantaneous MTBF. Figure 1: Expected Number of Failures vs. Time Figure 2: Instantaneous MTBF vs. Time RGA’s QCP also provides point estimates for these metrics given time. In addition, you can generate charts and results for the cumulative MTBF and similar output for instantaneous and cumulative failure rates.