Introduction to Weibull

Relyence Weibull provides a framework for you to manage and perform Weibull, or Life Data, analysis, as well as Reliability Growth Analysis (RGA). Weibull analysis is used on a wide variety of life data, such as design and development data, test data, or field data, in order to predict failure trends and analyze system behavior. Reliability Growth Analysis provides a methodology to track the effectiveness of the improvements made over time on a product's reliability performance. 

We recommend going through Getting Started with Relyence Weibull as a starting point for learning Relyence Weibull. From there, you can proceed to building your own analyses. The following process is intended to be a starting point; you can adapt it as required for your needs.

1. Gather your data

First, you must gather the data you wish to analyze. Spend time to gather a solid set of life data or reliability growth data, paying careful attention to how you identify failures, the scale you want to use for failure measurement, and how you plan to continually track your system failures for future analysis. For the simplest case, you have a list of failure times of your system. You may also have additional information, such as intervals, groups, and suspensions which can also be used for detailed life data analysis.

2. Create your Weibull Data Sets

Once your data is gathered, you enter the information in either a single Life Data Set, multiple Life Data Sets, a single Reliability Growth Data Set, or multiple Reliability Growth Data Sets. This is a tabular format that is used for life data analysis or reliability growth analysis. 

3. Select the calculation parameters

You select the Weibull life data or reliability growth calculation parameters to use for analysis.

For Life Data analysis, first, you set the distribution to use. You can either choose to perform a Best Fit analysis or select the distribution you prefer. When using the Best Fit analysis, the best fit distribution for your data will be determined. You can then use this as your selected distribution.

You can also directly enter in your choices for estimation method and median ranking method.

For Reliability Growth analysis, you specify the Termination Time.

4. Perform the life data analysis or reliability growth analysis

Once the calculation parameters are set, you can perform a calculation and see the results.

For Life Data analysis, the resulting data parameters are dependent on the selected Distribution. For example, the resulting parameters for the Weibull distribution include the shape parameter, the scale parameter, and the location parameter. The parameters define the shape of the curve fit of the data set.

For Reliability Growth analysis, the resulting data parameters are based on the Crow-AMSAA reliability growth model.

5. Analyze the Weibull plot and results

Lastly, once results are calculated, Life Data analysis plots or Reliability Growth Analysis plots can be viewed and analyzed.

You can choose the type of plot you wish to see.

• For Life Data analysis, you can review plots including a probability or PDF plot. When viewing the Life Data plots, you can assess the failure trend of your data, compute failure metrics, and make critical decisions in order to optimize your system performance.
• For Reliability Growth analysis, you can review plots including Cumulative Failure Rate or Instantaneous MTBF.