Have you ever struggled to perform a reliability analysis with very limited data? Discover Weibull Analysis, a reliability engineering technique that performs well with limited data and provides detailed results for a range of component types and environments. 

About Weibull Analysis 

Weibull Analysis is a statistical analysis that is used to determine reliability characteristics and trends from field and/or test failure data. It allows decisions to be made based on a limited amount of data. The Weibull distribution can be fit to datasets exhibiting an increasing, decreasing, or constant failure rate, a unique factor that separates it from other statistical distributions.  

What Weibull Analysis Can Accomplish that Other Analyses Cannot 

The Weibull analysis framework combines a low initial learning curve and low initial data requirements with flexibility to provide full statistical rigor as more data is gathered. 

With some initial assumptions, Weibull methods are easy to learn and start an analysis with.  For most parts, assumptions can then be refined away and part reliability modeled in more nuanced ways all within the Weibull analysis framework.  The Weibull analysis framework combines a low initial learning curve and low initial data requirements with flexibility to provide full statistical rigor as more data is gathered.  This flexibility and ubiquity also means that there are many methods available to determine confidence levels of the analysis as well as determining the few cases where a more specific distribution should be used.

Weibull Analysis Use Cases 

The results of a Weibull analysis can be used for a number of purposes. From a managerial standpoint, many decisions related to life-cycle costs and maintenance can benefit from the reliability estimates generated in this analysis. For example, a Weibull analysis will reveal the point at which a specific percentage of the population fails, which is valuable data for estimating when specific parts/assemblies should be serviced or replaced. Additionally, this analysis will also help to determine the optimal warranty period that minimizes customer dissatisfaction while also preventing excessive replacement costs. From a design perspective, a Weibull analysis can help determine the root cause of a specific failure, such as unanticipated or premature failures. 

Anomalies in the plotted data indicate when specific items are experiencing uncharacteristic failures compared to the rest of the population. The part numbers (or other distinguishing characteristic) of these items can then be used to determine the cause of the failure, which can include a bad production run, poor maintenance practices or unique operating conditions. 

Reliability Analyses Benefited by Weibull 

Weibull Analysis provides the advantage of understanding the time and rate at which components will fail, which is also valuable to many  reliability analyses including:  

The bottom-up failure analysis of a system, evaluating all possible failure modes of basic level components and assessing the potential impact that each failure may have on the overall system.
A top-down failure analysis, identifying combinations of lower-level failures that have the potential to cause a high-level undesired event (e.g., system failure).
A process to improve product reliability by systematically inducing failures through testing, then performing a failure analysis to identify the root cause of failure such that it can be removed or mitigated through the implementation and verification of a corrective action. (Email Qinfo@Quanterion.com to ask about upcoming training in this area.)
The practice of matching the preventive maintenance capabilities of an organization to the design characteristics of a product, thereby optimizing the cost-effectiveness of the maintenance program while ensuring the longevity of the product.
An analysis to determine the necessary inventory of spare parts based on the rate at which failures occur as well as other operational factors (i.e., number of fielded systems, use environment, cost of individual spares, etc.)

Common Misconceptions 

Often, Weibull analysis is underutilized because it is assumed to be overly complex. However, an analyst can typically realize the benefits of a Weibull analysis without a strong statistical background.     

The Weibull distribution provides a good fit to the data in most cases. Values for the distribution’s parameters allow the analyst to understand the failure characteristics of a part/system, providing the basis for cost-saving decisions throughout design, development and deployment.  

Interested to learn more about Weibull Analysis? View Weibull Analyis resources.  


Become Trained in Weibull Analysis! 

Sign up for Weibull Analysis training hosted Sept. 14-16 in Albuquerque, NM!  

Hurry! The deadline to register is Sept. 1, 2023! 

You will become proficient in: 

    1. – Performing a Weibull analysis
    1. – Interpreting the results
    1. – Applying the results to real-life situations
      1. – And more!

You will also learn the essential methods and techniques of Weibull analysis, and how to use Weibull analysis software to apply these methods to specific problems at your company. View the Weibull Analysis course outline for additional information about the course content.  

“Instructors were extremely knowledgeable about the subject matter and really brought something extra to the course by relaying real life examples and experiences using Weibull Analysis.”
Wes Fulton is founder and CEO of Fulton Findings™. Mr. Fulton worked as an aircraft – actuation – projects program engineer employed at AiResearch Los Angeles Division, AlliedSignal Aerospace Corporation (now Honeywell), and Moog for 16 years. As a program engineer for maneuvering fly-by-wire flight controls, he had engineering and management responsibility for Taiwan’s Indigenous Defensive Fighter leading edge flap actuation system (LEFAS) development and production, the Rockwell/MBB X-31A LEFAS flight test program, and General Dynamics’ F-16 Fighting Falcon LEFAS deployment support. While at AiResearch, he co-patented a multi-fuseable shaft for use in a high performance drive-train. Mr. Fulton has over 25 years of programming experience as a private programmer. He developed the first widely-used Weibull Engineering software, WeibullSMITH™. He wrote SuperSMITH™, the widely adopted software package for variability analysis, including WinSMITH™ Weibull and WinSMITH Visual programs. His assurance-index concept combines reliability and confidence into a single metric. His Justified-Likelihood-Function (JLF) and Fulton-Factor (FF) reduce bias in design-comparison applications. He has presented Dr. Bob Abernethy’s Weibull-Lognormal Analysis Workshop hundreds of times for businesses, government organizations, and engineering societies in the U.S. and internationally. He is a contributor to Dr. Abernethy’s standard Weibull reference, The New Weibull Handbook(c). He received his B.S. in mechanical engineering from Georgia Tech and his M.S. in mechanical engineering from California State University at Long Beach.

Carl Tarum is the Director of Software Research at Fulton Findings. His prior experience at Nexteer Automotive included ISO 26262 Functional Safety for Electric Power Steering. Prior roles included Design for Six Sigma Master Black Belt, where he taught, coached, and managed certification records. As a Reliability Engineer, he developed life testing and reliability predictions for hydraulic systems and new development. He has four patents related to steering systems. Additionally, Mr. Tarum developed the SuperSMITH® YBath™ and Help modules. He has conducted extensive research on p-value estimates for goodness of fits and methods to analyze mixtures of failure modes. He received his B.S.M.E from Montana State University, and his M.B.A from the University of Michigan – Flint.

Sign up for the Weibull Analysis training by Sept. 1, 2023!

Group discounts are available.  


To schedule an instructor-led in-person training course on Weibull Analysis, email qinfo@quanterion.com or call us at 877.808.0097 (toll free) or 315.732.0097.

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