Complete Array of Tools for Related Analyses
Degradation Data Analysis
The Weibull++ degradation analysis folio allows you to extrapolate the expected failure times of a product based on measurements that reflect how some performance measure (e.g., increase in crack propagation, decrease in tread depth, increase in vibration, etc.) has degraded for sample units over a period of time. The software offers a choice of the Linear, Exponential, Power, Logarithmic, Gompertz or Lloyd-Lipow models to analyze the degradation data, and generates Degradation vs. Time plots on either a linear or logarithmic scale.
Now in Version 10, you can create your own
user-defined degradation models. We've also added a new destructive
degradation folio for situations in which some or all of the units
under test are measured only once (e.g., if the test destroys the
Warranty Data Analysis
Weibull++'s popular warranty analysis folio converts warranty claims data (sales and returns) that are readily available in many organizations into failure/suspension data sets that can be analyzed with traditional life data analysis methods. You can use this analysis to better understand the failure behavior of products in the field and to generate forecasts of future returns that will be covered under warranty. The software provides a choice of data entry formats to fit your particular needs: Nevada Chart, Times-to-Failure, Dates of Failure or Usage. The folio provides all of the special options you need to analyze the data in a way that’s appropriate for the available data and your organization’s warranty fulfillment practices. For example:
- The Use Subsets option allows you to deal with non-homogeneous populations by analyzing data from different design iterations simultaneously and performing forecasts based on mixed sales data.
- The Suspend After option allows you to take into account the possibility that failure data were not collected beyond the specified warranty period and/or to exclude predicted failures that will not be covered under warranty.
- The Statistical Process Control feature (available for Nevada chart folios) can automatically detect abnormal sales or return periods and color-code the results to highlight specific data points you may wish to investigate further.
- The Usage format allows you to enter returns data in terms of the amount of usage accumulated (e.g., mileage, cycles, etc.) rather than time in service. In the Synthesis version, we have enhanced this feature by providing additional tools to help you configure the analysis to appropriately estimate the likely usage for units that are still operating in the field (i.e., suspensions).
Non-Parametric Life Data Analysis
The non-parametric LDA folio offers a choice of three methods for analyzing life data without assuming an underlying life distribution: Kaplan-Meier, Simple Actuarial and Standard Actuarial. This folio may be useful when dealing with unknown failure modes, when there is not enough data to assume a life distribution or when the data set does not fit any life distribution in a satisfactory way. Now Weibull++ also performs a parametric analysis directly within the same folio using the unreliability estimates that are generated by the non-parametric analysis.
Target Reliability Estimator Based on Costs vs. Benefits
Choosing an optimal reliability goal involves deciding on important trade-offs. For example, higher reliability typically requires higher production costs, but higher reliability will typically also lead to lower warranty costs and higher market share. The new Target Reliability tool generates multiple plots that will help you select a target reliability that will minimize cost, maximize profit and maximize the return on an investment that affects reliability.
Reliability Test Design
Weibull++ now offers a new Test Design Assistant that helps you select which reliability test design tool(s) will meet your specific needs. This includes one new method for demonstration test design (Non-Parametric Bayesian) and two new tools that help to visualize the outcomes from planned tests.
- The Reliability Demonstration Test Design tool has been completely redesigned and expanded. You can use the Parametric Binomial, Non-Parametric Binomial, Exponential Chi-Squared or Non-Parametric Bayesian methods to help choose the right test time/sample size for a reliability demonstration test. These methods can be used for designing a zero-failure test (where the reliability target is demonstrated if you don’t observe any failures during the test) and for tests with other quantities of "allowable failures" (e.g., one-failure test, two-failure test, etc.)
- The Expected Failure Times Plot provides a visual depiction of the failure times you can expect to observe when you implement a particular test plan. If you perform the test and enter the actual failures as they are observed, you can use the plot to monitor whether the test is proceeding as expected or receive an early warning that adjustments may be needed.
- The Difference Detection Matrix calculates how much test time is required before it is possible to detect (and demonstrate) a statistically significant difference in the life of two product designs.