Engineered Software

Weibull Analysis


Life Data Analysis

Maintenance Optimization

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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.