# Weibull analysis formula

Life Data Analysis is a method of predicting how your product will operate through its lifetime by analyzing a sample set of failure data. The analysis is done by curve fitting the sample data set to a distribution, and using that distribution to determine trends. To perform Life Data Analysis, you must have some sample data about the product you want to analyze. The sample data is typically information related to product failures or product performance.

Once the data is collected, you then determine a mathematical distribution and its associated parameters that fits the data captured.

Using this curve, you can then generate a graph of the data and its best-fit distribution. Using the graph and the distribution-specific parameters, you can analyze and predict future performance based on the distribution curve plotted. The analysis process and techniques are the same for both. The term Weibull Analysis has arisen and is commonly used because the Weibull distribution is 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. Therefore, Weibull Analysis, like Life Data Analysis, is a statistical-based technique used to analyze various types of life data in order to predict failure trends.

The key part of the statistical analysis is done by using mathematical distributions, one of which is the Weibull distribution. The Weibull distribution is especially noteworthy due to its versatility, its ability to model life data, and its ability to work with a small data set. It is one of the most widely used mathematical techniques for evaluating life data across a range of industries, and across the product lifecycle.

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. Weibull Analysis offers a valuable way to gain insight into the lifetime performance of your product.

By using sample data captured about failures and time, that information can be effectively analyzed using Weibull techniques to help answer critical concerns. For example, you can analyze the expected life of a product, how long warranty periods should last, and identify the root cause of a device failure such as a design flaw, improper maintenance, or a bad production run. Weibull Analysis helps to identify these types of problems and many more.

The primary advantage of Weibull Analysis is the ability to analyze failure trends and provide failure forecasts based on known sample data sets.

The benefit of the Weibull distribution specifically is due to its versatility and ability to effectively be applied to small sample sets. Another advantage of Weibull Analysis is that it offers a visual and easily understood graphical view of failure data. Weibull plots are very useful in discovering trends and allowing analysts to easily present and describe important information in a concise, clear format. 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, operating intervals, 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.

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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.

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. A central component of Weibull Analyses are Weibull plotsor the resulting graphical representation of your failure data along with the distribution curve. Weibull plots are a vital element of Weibull tools, allowing you to visually see your life data along with the distribution line for full understanding of trends and future performance.

In some cases, you may want to statistically determine which distribution best fits your data instead of selecting a particular distribution. In this case, a best fit analysis can be done.

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Best fit analysis considers each distribution and then using statistical techniques determines which one most closely aligns with the sample data.

The best fit is also a useful tool when you are unsure of which distribution to use.

Often, if a Weibull Analysis software tool is used to perform life data analysis, a best fit analysis feature is available. The best fit tool will consider each distribution and provide a numerical measure of how closely it fits your data.Almost every major item that consumers purchase has a warranty period. In most cases, you are encouraged by the seller to purchase an extended warranty or protection plan. Your first thought may be that if an extended warranty is needed, then this product is likely to fail just after the factory warranty has expired.

It may cause you to doubt the quality of the product or questions how the warranty terms or the costs of extended warranties are determined. Warranty terms and conditions are generally based upon calculated risks of failure. Many companies and design engineers utilize a statistical tool called life data analysis otherwise known as Weibull Analysis.

By determining the risk of a product or component failure, the manufacturer can better estimate warranty costs over time and assign a corresponding warranty period. Weibull Analysis is a methodology used for performing life data analysis.

Weibull Analysis is an effective method of determining reliability characteristics and trends of a population using a relatively small sample size of field or laboratory test data.

Depending upon the product or industry, product life data is calculated in hours, miles, number of cycles or other metrics used to establish a measure of successful function of a product. The method is named for Mr. Waloddi Weibull who in invented the Weibull distribution. He presented a paper on the subject in Initial reaction to the paper initially ranged from uncertainty to total rejection.

However, others in the field began to utilize and improve the method resulting in it being implemented by the U. Air Force in the s, and later by the automotive industry. In industry today, Weibull Analysis is the foremost method for evaluating life data. There are different types of life data and each type provides varying information regarding the product lifecycle. Analysis methods used vary according to the data type. The three-parameter PDF examines:.

Although the calculations can be performed manually, there are various software packages and tools available to perform the calculations and generate the graphs.

In the following example, a well-known software package was used. When the b value is represented on a graph, it is evident why it is termed the slope. The Shape Parameter is one of the most widely examined parameters because it helps indicate the types of failures occurring base on slope or the b value. Most companies in business today monitor warranty costs and product failure rates. The goal is to reduce warranty costs and possible loss of brand equity. In addition, information gathered using a Weibull Analysis allows the manufacturer to plan for any known costs or set the proper warranty terms.

The Weibull Analysis is a valuable and relatively easy to apply tool that can be utilized by reliability engineers or analysts. The data set distribution may be used to evaluate product reliability, determine mean life, probability of failure at a specific time and estimate overall failure rates.

In previous sections, we reviewed some history of the Weibull Analysis, types of data, a definition of Weibull Analysis and why it is frequently used. In this section, we will investigate how to perform or complete a Weibull, or Life Data, Analysis. There are four main steps in performing a Weibull Analysis:. This example will analyze life data for motors in machinery currently in-use in the field. The unit performance is a function of running time in years.

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The first step is to examine the distribution ID plot of the data and select the line that best fits our data.The probability density function of a Weibull random variable is: [1]. Its complementary cumulative distribution function is a stretched exponential function. If the quantity X is a "time-to-failure", the Weibull distribution gives a distribution for which the failure rate is proportional to a power of time.

The shape parameter, kis that power plus one, and so this parameter can be interpreted directly as follows: [3]. In the field of materials sciencethe shape parameter k of a distribution of strengths is known as the Weibull modulus. Applications in medical statistics and econometrics often adopt a different parameterization.

A third parameterization can also be found. The form of the density function of the Weibull distribution changes drastically with the value of k. Moreover, the skewness and coefficient of variation depend only on the shape parameter.

A generalization of the Weibull distribution is the hyperbolastic distribution of type III. The cumulative distribution function for the Weibull distribution is. The failure rate h or hazard function is given by. The moment generating function of the logarithm of a Weibull distributed random variable is given by [8].

Similarly, the characteristic function of log X is given by. In particular, the n th raw moment of X is given by. The mean and variance of a Weibull random variable can be expressed as.

The kurtosis excess may also be written as:. A variety of expressions are available for the moment generating function of X itself. As a power seriessince the raw moments are already known, one has. The characteristic function has also been obtained by Muraleedharan et al. The information entropy is given by. The fit of a Weibull distribution to data can be visually assessed using a Weibull plot.

The reason for this change of variables is the cumulative distribution function can be linearized:. Therefore, if the data came from a Weibull distribution then a straight line is expected on a Weibull plot.Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising.

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Learn more. Published on Nov 20, A simple introduction to reliability analysis of components. Though this lacks explanations of the calculated steps it shows how simple analysis can be. Note that it only addresses the Weibull distribution.

It does share how to look elsewhere if the Weibull shape parameter is not near the ideal three 3. SlideShare Explore Search You. Submit Search. Home Explore.

## Life Data Analysis (Weibull Analysis)

Successfully reported this slideshow. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime. Using microsoft excel for weibull analysis. Upcoming SlideShare. Like this document? Why not share! Embed Size px.

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Start on. Show related SlideShares at end. WordPress Shortcode. Full Name Comment goes here. Are you sure you want to Yes No. Apicha Buranatun. No Downloads. Views Total views.You can report issue about the content on this page here Want to share your content on R-bloggers? I will look at the problem from both a frequentist and Bayesian perspective and explore censored and un-censored data types. Fair warning — expect the workflow to be less linear than normal to allow for these excursions.

First — a bit of background. FDA expects data supporting the durability of implantable devices over a specified service life. Engineers develop and execute benchtop tests that accelerate the cyclic stresses and strains, typically by increasing the frequency. Such a test is shown here for a coronary stent: 1. The most common experimental design for this type of testing is to treat the data as attribute i.

By treating each tested device as a Bernoulli trial, a 1-sided confidence interval can be established on the reliability of the population based on the binomial distribution. This approach is not optimal however since it is generally only practical when all tested units pass the test and even then the sample size requirement are quite restricting. Additionally, designers cannot establish any sort of safety margin or understand the failure mode s of the design.

We can do better by borrowing reliability techniques from other engineering domains where tests are run to failure and modeled as events vs. Such data often follows a Weibull distribution which is flexible enough to accommodate many different failure rates and patterns. In the following section I work with test data representing the number of days a set of devices were on test before failure. All devices were tested until failure no censored data. There are data points, which is more than typically tested for stents or implants but is reasonable for electronic components.

Once the parameters of the best fitting Weibull distribution of determined, they can be used to make useful inferences and predictions.

### WEIBULL function

The parameters we care about estimating are the shape and scale. Lognormal and gamma are both known to model time-to-failure data well. They are shown below using the denscomp function from fitdistrplus. Goodness-of-fit statistics are available and shown below for reference.This site uses cookies to give you a better experience, analyze site traffic, and gain insight to products or offers that may interest you.

By continuing, you consent to the use of cookies. Learn how we use cookies, how they work, and how to set your browser preferences by reading our Cookies Policy. The Weibull distribution is both popular and useful. It has some nice features and flexibility that support its popularity. This short article focuses on 7 formulas of the Weibull Distribution. If you want to know more about fitting a set of data to a distribution, well that is in another article. It has the essential formulas that you may find useful when answering specific questions.

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The 2-parameter Weibull distribution has a scale and shape parameter. The 3-parameter Weibull includes a location parameter. It is defined as the value at the It is also known as the slope which is obvious when viewing a linear CDF plot. F t is the cumulative probability of failure from time zero till time t. Very handy when estimating the proportion of units that will fail over a warranty period, for example. R t is the chance of survival from from time zero till time t.

Instead of looking for the proportion that will fail the reliability function determine the proportion that are expected to survive. The m x function provides a means to estimate the chance of survival for a duration beyond some known time, t, over which the item s have already survived.

What the probability of surviving time x given the item has already survived over time t? This is the cumluative failure rate from time zero till time t, or the area under the curve described by the hazard rate, h t.

Also published on Medium. Receive a weekly email alerting you to recently published tutorials in the CRE Preparation Notes series. Your email address will not be published. If you are a human and are seeing this field, please leave it blank. Thanks, — JPE. Good catch Jay, thanks for pointing this out. Thank you for the Article. It helped me a lot.

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