Weibull++ Example 10 - Competing Failure Modes Analysis
Background
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. The objective of this test is to determine the overall reliability for the component at 100,000 cycles.
Experiment and Data
(This example has been abstracted from Example 15.6 from the Meeker and Escobar text book Statistical Methods for Reliability Data, published by John Wiley and Sons.)
The following tables show the time-to-failure data for each mode and the suspension data. The failure mode that is due to random voltage spikes is denoted by a V in the table. The failure mode that is due to wear-out failures is denoted by a W in the table.
| Number in State | Failure Time* | Failure Mode | Number in State | Failure Time* | Failure Mode | |
| 1 | 2 | V | 1 | 147 | W | |
| 1 | 10 | V | 1 | 173 | V | |
| 1 | 13 | V | 1 | 181 | W | |
| 2 | 23 | V | 1 | 212 | W | |
| 1 | 28 | V | 1 | 245 | W | |
| 1 | 30 | V | 1 | 247 | V | |
| 1 | 65 | V | 1 | 261 | V | |
| 1 | 80 | V | 1 | 266 | W | |
| 1 | 88 | V | 1 | 275 | W | |
| 1 | 106 | V | 1 | 293 | W | |
| 1 | 143 | V | 8 | 300 | suspended |
Analysis
Step 1: Using Weibull++ 7, the first step is to create a new Data Entry Spreadsheet to hold grouped times-to-failure data with suspensions.
Step 2: Using the Subset ID column to identify the failure mode, enter the data into the Folio. Select the Weibull distribution with Competing Failure Modes (CFM) and MLE, as shown next.

Step 3: Click Calculate. In the Competing Failure Modes Select Subsets window that appears, identify Mode 1 as the V failure mode and Mode 2 as the W failure mode, as shown next, and click OK.

Step 4: Open the Quick Calculation Pad and determine the overall reliability of the component at 100,000 cycles, as shown next.

The overall reliability of the component at 100,000 cycles is estimated to be 69.1%.


