Application Examples for Weibull++
ReliaSoft's Weibull++ life data analysis software provides an extensive array of tools to help you understand and communicate how a product will perform over time. Some of the many useful applications include the ability to:
- Compare suppliers or designs based on reliability.
- Demonstrate that an item meets specified reliability.
- Make predictions about performance during the useful life (or warranty) period.
- Use plots and other reports to effectively communicate performance estimates to management.
- And much more.
These examples demonstrate some of the types of analyses you can perform with this application. For additional product documentation, including the Quick Start Guide, visit the Synthesis eDocs & ePubs Library.
Examples Using Standard Folios
Complete and Right Censored Data Analysis
Ten identical units are reliability tested at the same application and operation stress levels for 120 hours. The objective is to use the complete and right censored data from the test to determine the unreliability for a mission duration of 226 hours and the warranty time for a reliability of 85%.
Analyzing Complete Data
Six units are tested to failure. The objective is to use the complete data from the test to obtain a probability plot with 90% 2-sided confidence bounds and a pdf plot.
Analyzing Sudden Death Data
A manufacturer uses data from a sudden death test to demonstrate the reliability for a product with a 70% lower one-sided confidence level. Four groups of 5 units each are tested sequentially until the first failure in each group.
Examples Using Specialized Folios
Degradation Analysis Example
Five turbine blades were tested for crack propagation. The test units were cyclically stressed and inspected every 100,000 cycles for crack length. Failure is defined as a crack of length 30 mm or greater.
A company keeps track of its shipments and warranty returns on a month-by-month basis. Using the warranty analysis folio, determine the parameters for a 2-parameter Weibull distribution and predict the number of products from each of the three shipment periods that will be returned under warranty in October.
Factory Equipment Failure Log
A manufacturer is tracking the failures occurring in the product line. When a failure occurs in a machine, the time of occurrence and the time of repair are recorded for the machine and the parts that failed. Using Weibull++’s event log folio, determine the failure and repair distributions.
Non-Parametric Life Data Analysis
A group of 55 units are put on a life test during which the units are evaluated every 50 hours. Use the simple actuarial method to determine reliability estimates for each failure time.
Non-Parametric Recurrent Event Data Analysis for Transmission Repair
Repairs are tracked on automatic transmissions in a sample of 34 cars in a preproduction road test. The objective is to determine the mean cumulative number of repairs per car by 24,000 test miles (equivalently 5.5 x 24,000 = 132,000 customer miles).
Parametric Recurrent Event Data Analysis Example
The repairs made to a system are tracked over a period of time. Each repair is capable of addressing any damage accumulated up to the time of the current failure. The objective is to determine how effective the repairs are.
Competing Failure Modes Analysis
An electronic component has two failure modes. One failure mode is due to random voltage spikes, which cause failure by overloading the system. The other failure mode is due to wear-out failures, which usually happen only after the system has run for many cycles. Using competing failure modes analysis, the overall reliability for the component at 100,000 cycles is determined.
Failure Modes RBD Analysis
A particular component can fail due to six independent primary failure modes: A, B, C, D, E and F. The component fails if mode A, B or C occurs. If mode D, E or F occurs alone, the component does not fail; however, the component will fail if any two (or more) of these modes occur (i.e., D and E; D and F; E and F). The objective is to analyze each data set using the 2-parameter Weibull distribution with MLE and to determine the lower 1-sided 90% confidence interval on the reliability of this component at 100 hours.