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Weibull Analysis
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Weibull analysis is a powerful tool that can be used to classify failures and to model failure behavior. Weibull analysis involves fitting a time to fail distribution to failure data. There are several methods for doing this, and the software provides 4 methods:

1. Maximum likelihood estimation (MLE),
2. Probability plotting,
3. Hazard plotting, and
4. Modified moment estimation.

After Weibull analysis is completed, the value of the shape parameter, b , can be used to classify failures. A shape parameter of less than 1.0 indicates infant mortality failures. The causes of infant mortality failures are:

· Improper use,
· Improper installation
· Improper setup
· Inadequate training
· Poor quality control
· Defective materials
· Power surges
· Improper testing

In this case, there are two approaches for improving reliability. The equipment or component can be "burned-in" (burn-in refers to running the component for a period of time to weed out items with short lives. This is common for manufacturers of electronic devices). Or, personnel can be trained on proper setup, installation, inspection, etc.

A shape parameter equal to 1.0 indicates random failures. The only way to increase the reliability of the equipment in this case is by redesign.

A shape parameter greater than 1.0 indicates wearout failures. In this case, reliability and cost performance can be improved by optimizing the preventive maintenance schedule.

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