Weibull++ supports all types of life data analysis, including:
Parameter estimation options for standard life data analysis include:
All major lifetime distributions (including all forms of the Weibull distribution) are also supported:
If you are not sure, which model is appropriate for a given data set, the Distribution Wizard automatically performs several types of goodness-of-fit tests (Kolmogorov-Smirnov, Correlation Coefficient, Likelihood value) in order to rank the available distributions. It comes also with the ability to rank data points using Kaplan-Meier or Median Ranks.
Weibull++ provides confidence bounds for parameters, calculated results and plots. Depending on the specific analysis method used, the confidence bounds may be calculated using the Fisher Matrix, Likelihood Ratio, Beta Binomial or Bayesian (BSN) approach.
Reliability demonstration test design determines the appropriate sample size, test duration or other variables for designing effective reliability tests and demonstration tests. Expected failure times plot displays times-to-failure that you may expect to observe for a given reliability life test.
Difference detection matrix allows you to detect the difference between the reliabilities of two separate designs.
A choice of data entry formats available for using sales/returns data to perform life data analysis and make warranty projections: nevada chart, times-to-failure, dates of failure and usage (e.g., mileage, cycles, etc.), as well as the time.
Use the linear, exponential, power, logarithmic, Gompertz or Lloyd-Lipow models to extrapolate the failure times of a product based on its performance (degradation) over a period of time. Weibull++ also includes destructive degradation analysis and the option to create user-defined degradation models.
Weibull++ supports a variety of experiment design types, including factorial and fractional designs, Taguchi robust designs, response surface method designs, and DOE based on product life, called reliability DOE.
The Quick Calculation Pad (QCP) is a "Calculation Log" that allows you to record the results from a series of different calculations and then copy/paste the information as needed.
The software also provides a complete array of calculated results and plots based on the analysis. For life data analysis, this includes:
Weibull++ provides a complete array of advanced plotting tools. The Plot Setup allows you to completely customize the "look and feel" of plot graphics. You can save your plots in a variety of graphic file formats for use in other documents.
Customizable reports are also built into Weibull++. They seamlessly integrate spreadsheet and word processing capabilities while enabling you to include calculated results and plots from your analysis.
Use the General Renewal Process (GRP) model or the Mean Cumulative Function (MCF) to analyze recurrent event data.
Use Kaplan-Meier, Simple Actuarial or Standard Actuarial techniques when analyzing incomplete data that does not fit any life distribution in a satisfactory way.
Use the specialized Event Log Folio to convert system failure and repair data into times-to-failure and times-to-repair.
Automatically perform analyses on simulated data sets in order to investigate confidence bounds, testing scenarios and help solve many other reliability engineering questions.
It provides a powerful opportunity to reduce a system's maintenance costs while maximizing uptime. The new Maintenance Planning Tool generates a cost vs. time plot designed to help you determine the most cost-effective time to replace a system’s worn or failed components. The tool also offers an option to create preventive and/or inspection tasks for use in BlockSim’s simulation diagrams.
Estimates the parameters of any user-defined non-linear equation. This gives you the flexibility to perform simple parameter estimation on statistical models other than the life distributions and life-stress relationship models available in standard folios. After you have solved the equation, the tool provides a plot to visualize how the data fit the function, and also makes it easy to calculate Y for any given X value.
Quickly solves for the root of any user-defined non-linear equation. This helps you eliminate some of the guesswork of solving for the value of the unknown variable that makes the function be as close to zero as possible.
Estimates the parameters of a statistical model based on what you know about the behavior over time. The software will "translate" your answers into the inputs required to obtain the model parameters.
Frees you from tedious lookups in tables by quickly returning results for commonly used statistical functions. Results include Median Ranks, Chi-Squared Values, Cumulative Binomial Probability, and many more. There is also a Polynomial Interpolation Function that allows you to enter known data points and then calculate Y for any given X value.