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Weibull Blog

The Quick Guide to Perform a Weibull Analysis [one-page infographic]

In this blog, we designed a one-page infographic to visualise the entire process of Weibull Analysis. If you want to learn it in detail, check out our three-part Weibull Analysis series, which reviewed how to perform a Weibull Analysis step by step, from data preparation, lifetime distribution selection and parameter estimation to validation and reliability improvement.

What is Weibull Analysis and Why it is important?    

No organisation can eliminate all failures from a design or operation. Since different components have different failure patterns, it is vital to identify the most likely failures and then identify appropriate actions to mitigate the effects of those failures. That makes reliability engineering important for every physical asset that is critical to the organisation or the function of a system.  

How to quantify Reliability and predict the component’s future performance? The answer lies in Weibull Analysis.  

Weibull Analysis, also known as life data analysis, is an effective methodology of determining reliability characteristics of a population (e.g., reliability or probability of failure at a specific time, the mean life and the failure rate) by fitting a statistical distribution to life data from a relatively small but representative sample of units. 

How to Perform a Weibull Analysis?

Generally, Weibull Analysis requires the reliability engineers to: 

  • Gather life data for the product. 
  • Select a lifetime distribution that will fit the data and model the life of the product. 
  • Estimate the parameters that will fit the distribution to the data. 
  • Generate plots and results that estimate the life characteristics of the product, such as the reliability or mean life. 

More specifically, we can perform a Weibull Analysis in 10 steps. 

  1. Determine the asset(s) to be analysed. 
  2. Determine the component failure mode for that asset(s). 
  3. Obtain as much relevant life data as practical. 
  4. Classify life data. 
  5. Select the right lifetime distribution that will fit the life data set and model the life of the component. 
  6. Estimate the parameters of the life distribution that will make the function most closely fit the life data set. 
  7. Generate plots and calculate the functions of certain distribution. 
  8. Indicate Confidence Bounds. 
  9. Review the Analysis in 4 aspects: practical, graphical, analytical, and confidence. 
  10. Determine and implement appropriate strategies. 

Weibull Analysis Process One-page Infographic

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Weibull Analysis Related Resources:

Blog: 

  1. How to Perform a Weibull Analysis (Part 1 of 3) – Data Preparation 
  2. How to Perform a Weibull Analysis (Part 2 of 3) – Lifetime Distribution Selection and Parameter Estimation
  3. How to Perform a Weibull Analysis (Part 3 of 3) – Validation of Results and Reliability Improvement

Weibull Analysis Software: ReliaSoft Weibull++ – Provide the most comprehensive toolset available for reliability life data analysis, calculated results, plots and reporting.  

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