Accendo Reliability

Your Reliability Engineering Professional Development Site

  • Home
  • About
    • Contributors
  • Reliability.fm
    • Speaking Of Reliability
    • Rooted in Reliability: The Plant Performance Podcast
    • Quality during Design
    • Way of the Quality Warrior
    • Critical Talks
    • Dare to Know
    • Maintenance Disrupted
    • Metal Conversations
    • The Leadership Connection
    • Practical Reliability Podcast
    • Reliability Matters
    • Reliability it Matters
    • Maintenance Mavericks Podcast
    • Women in Maintenance
    • Accendo Reliability Webinar Series
  • Articles
    • CRE Preparation Notes
    • on Leadership & Career
      • Advanced Engineering Culture
      • Engineering Leadership
      • Managing in the 2000s
      • Product Development and Process Improvement
    • on Maintenance Reliability
      • Aasan Asset Management
      • AI & Predictive Maintenance
      • Asset Management in the Mining Industry
      • CMMS and Reliability
      • Conscious Asset
      • EAM & CMMS
      • Everyday RCM
      • History of Maintenance Management
      • Life Cycle Asset Management
      • Maintenance and Reliability
      • Maintenance Management
      • Plant Maintenance
      • Process Plant Reliability Engineering
      • ReliabilityXperience
      • RCM Blitz®
      • Rob’s Reliability Project
      • The Intelligent Transformer Blog
      • The People Side of Maintenance
      • The Reliability Mindset
    • on Product Reliability
      • Accelerated Reliability
      • Achieving the Benefits of Reliability
      • Apex Ridge
      • Metals Engineering and Product Reliability
      • Musings on Reliability and Maintenance Topics
      • Product Validation
      • Reliability Engineering Insights
      • Reliability in Emerging Technology
    • on Risk & Safety
      • CERM® Risk Insights
      • Equipment Risk and Reliability in Downhole Applications
      • Operational Risk Process Safety
    • on Systems Thinking
      • Communicating with FINESSE
      • The RCA
    • on Tools & Techniques
      • Big Data & Analytics
      • Experimental Design for NPD
      • Innovative Thinking in Reliability and Durability
      • Inside and Beyond HALT
      • Inside FMEA
      • Integral Concepts
      • Learning from Failures
      • Progress in Field Reliability?
      • R for Engineering
      • Reliability Engineering Using Python
      • Reliability Reflections
      • Testing 1 2 3
      • The Manufacturing Academy
  • eBooks
  • Resources
    • Accendo Authors
    • FMEA Resources
    • Feed Forward Publications
    • Openings
    • Books
    • Webinars
    • Journals
    • Higher Education
    • Podcasts
  • Courses
    • 14 Ways to Acquire Reliability Engineering Knowledge
    • Reliability Analysis Methods online course
    • Measurement System Assessment
    • SPC-Process Capability Course
    • Design of Experiments
    • Foundations of RCM online course
    • Quality during Design Journey
    • Reliability Engineering Statistics
    • Quality Engineering Statistics
    • An Introduction to Reliability Engineering
    • Reliability Engineering for Heavy Industry
    • An Introduction to Quality Engineering
    • Process Capability Analysis course
    • Root Cause Analysis and the 8D Corrective Action Process course
    • Return on Investment online course
    • CRE Preparation Online Course
    • Quondam Courses
  • Webinars
    • Upcoming Live Events
  • Calendar
    • Call for Papers Listing
    • Upcoming Webinars
    • Webinar Calendar
  • Login
    • Member Home

by Fred Schenkelberg Leave a Comment

Life Estimates Based on Supplier Data

Life Estimates Based on Supplier Data

Suppliers often include reliability information along with performance specifications.

We look for reliability statements as one part of the selection process to ascertain if the component is likely to have sufficient reliability.

When the vendor’s data is clearly stated and meaningful, that information saves us from potentially having to conduct our own reliability evaluations.

Let’s say we are exploring purchasing sealed bearing for a front tire of a new model of bicycle.

We expect the bearings reliability should be at least 98% reliable over 1 years with regular exposure to 40°C ambient temperatures.

We find a couple of options online and examine the data sheets.

Bearing Vendor A

The data sheet only provides the following statement concerning reliability.

MTTF 75,000 hours

Nothing else.

Is this good enough for our application? Let’s find out.

With no other information than MTTF we should use the exponential distribution to estimate the bearing reliability at one years.

$$ \large\displaystyle \begin{array}{*{35}{l}}
R\left( t \right)={{e}^{-\frac{t}{\theta }}} \\
R\left( 8,760 \right)={{e}^{-\frac{8,760}{75,000}}}=0.8897 \\
\end{array}$$

Where t is time in hours and a year has 8,760 hours.

And, $-\theta-$ is the MTTF value given by the vendor.

The result is disappointing. According to this information and the assumption of an exponential distribution, we would expect about 11% of the units to fail over the year.

Bearing Vendor B

The data sheet references a life document on the vendor’s website which contains the following reliability information.

The bearings primarily fail due to the breakdown of the bearing grease.

The grease breakdown (the document includes a definition of failure, which references in increase in start to movement resistance) follows a Weibull distribution time to failure distribution at 25°C ambient.

The Weibull parameters are $-\beta-$ = 2 and $-\eta-$ = 75,000 hours

We can use the reliability function for the Weibull distribution to estimate the probability of successfully operating over a year as follows

$$\large\displaystyle \begin{array}{*{35}{l}}
R\left( t \right)={{e}^{-{{\left( \frac{t}{\eta } \right)}^{\beta }}}} \\
R\left( 8,760 \right)={{e}^{-{{\left( \frac{8,760}{75,000} \right)}^{2}}}}=0.9865 \\
\end{array}$$

This is better, yet notice the parameters apply with a 25°C ambient temperature.

We expect the bearing will see a sustained temperature of 40°C. So, reading further in the vendor supplied life document we find a formula to translate the characteristic life, $-\eta-$ for different temperatures.

Without going into the details of the math associated with the provided formula we determine the $-\eta-$ value is reduced by 0.95, thus becomes 71,250 hours.

Recalculating the reliability at one year, we find at 40°C it is 98.5% reliable.

Vendor Selection

With just the information from the data sheets, supporting documents, and a little math the selection of the bearing based on expected reliability performance is clear.

The direct calculations show that one vendor will not meet the target reliability using the given information.

It is likely that Vendor A knows about bearing grease wear out and the relationship between temperature and characteristic life, yet choose to list the often requested and simple MTTF value.

Unfortunately, that leaves the reader to either make a few assumptions or spend time investigating if there is additional reliability information available.

The Vendor B data provided a clear means to calculate reliability for our conditions.

Even if the results didn’t meet our requirements, working with Vendor B would be my choice.

They seem to understand what is expected to fail, have done the characterization work, and freely share the reliability information.

They may have an alternative solution that would meet our needs (if needed).

The idea here is to use the vendor provided reliability data to estimate the reliability performance under your expected conditions.

Then compare to your reliability apportioned goals.


Related:

The Next Step in Your Data Analysis (article)

Confidence Interval Interpretations & Misunderstandings (article)

Basic Approaches to Life Testing (article)

 

 

Filed Under: Articles, Musings on Reliability and Maintenance Topics, on Product Reliability Tagged With: supplier

About Fred Schenkelberg

I am the reliability expert at FMS Reliability, a reliability engineering and management consulting firm I founded in 2004. I left Hewlett Packard (HP)’s Reliability Team, where I helped create a culture of reliability across the corporation, to assist other organizations.

« Confidence Interval for a Proportion – Normal Approximation
Interpolation within Distribution Tables »

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Article by Fred Schenkelberg
in the Musings series

Join Accendo

Receive information and updates about articles and many other resources offered by Accendo Reliability by becoming a member.

It’s free and only takes a minute.

Join Today

Recent Articles

  • test
  • test
  • test
  • Your Most Important Business Equation
  • Your Suppliers Can Be a Risk to Your Project

© 2025 FMS Reliability · Privacy Policy · Terms of Service · Cookies Policy