Weibull analysis is a methodology for analyzing various types of life data in order to predict failure trends. Weibull analysis is based on high-powered statistical analysis using different distributions, including, most notably, the Weibull distribution. The Weibull distribution is especially significant due to its versatility and its ability to model life data. It is one of the most widely used mathematical techniques for evaluating life data across a range of industries and across the product lifecycle.
Life data analysis
The core principle in Weibull analysis is to gather a sample set of life data, or data about failures over a time frame, and then apply Weibull techniques in order to fit the data to a distribution. Using this information, you can then extrapolate to evaluate trends, assess the probability of a system operating over a time interval, analyze the mean life of a system, predict failure rate, or even determine a warranty period.
For this reason, Weibull analysis is also referred to as “life data analysis”. Typically this methodology is referred to as Weibull analysis because the Weibull distribution can be very useful to characterize a wide range of data trends that other statistical distributions cannot, including decreasing, constant, and increasing failure rates. In addition, the Weibull distribution can effectively be used to approximate other distributions.
Why perform Weibull analysis?
Historical time and failure data can be effectively used to solve critical issues such as the expected life of a product, how long warranty periods should last, and in identifying the root cause of a device failure such as a design flaw, improper maintenance, or a bad production run. Relyence Weibull helps to identify these types of problems and much more. Here are just a few examples of the ways Weibull analysis can be applied:
- Predicting: Using your life data, Weibull plots can be used to predict future failure characteristics. An advantage of Weibull analysis is that it can be used on a small sample size.
- Analyzing: By evaluating the curves of your Weibull plot, you can uncover failure causes.
- Planning: Using the information gleaned from Weibull analysis, you can effectively plan and organize maintenance strategies.
- Forecasting: From information gained with Weibull analysis, you can forecast future needs, such as when spare parts will likely be needed.
How do I perform Weibull analysis?
Weibull analysis is performed by first defining a data set, or a set of data points that represent your life data. This data can be in many forms, from a simple list of failure times, to information that includes quantities, failures, suspensions, and more. The data is then evaluated to determine a best fit distribution, or the curve which best fits your data based on a statistical analysis. This may be the Weibull distribution, or a different distribution commonly supported in Weibull analysis such as the Normal, Lognormal, or Exponential distributions. You can then perform additional analysis, such as looking at confidence bounds based on selected confidence levels.
Almost all Weibull analyses are done using a specific software tool designed for the process. There are other means of performing Weibull analysis, but those are extremely limited in comparison to using a software tool. Look for a tool that provides an easy-to-use interface combined with plotting capability that is easy to read and interpret. A web-based package allows you access to your Weibull analyses across remote teams or distributed locations.
Relyence Weibull is a powerful tool for performing Weibull analyses that seamlessly integrates with other Relyence modules for optimal system reliability analysis. Offered on the web and supporting a mobile-friendly interface, Relyence Weibull is built with today’s technologies in mind.