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 Larry George Leave a Comment

Poll: “Is life data required…?”

Poll: “Is life data required…?”

My wife says I am wasting my time trying to change reliability statistics, so I polled the www.linkedin.com Reliability Leadership…, ASQRRD, IEEE Reliability, “Biostatistics, and No MTBF groups. The polls claimed that “Life data, censored or not, is required to estimate MTBF, reliability function, failure rate function, or survivor function. TRUE? FALSE? or DON’T KNOW.” I am grateful for the responses.

It’s normal to collect a random sample of a random variable to estimate its distribution function. The Lifetime Data Analysis Journal says that it is… “An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data.” IEEE1413.1-2002 5.2.1 reliability prediction guide says times to failures and “accumulated operating time for all items that have not failed” are required. CFR Title 21 Volume 8 Subpart B Section 821.20 “Devices Subject to Tracking”, requires times-to-failures for implantable medical devices. Www.Weibull.com says times to failure are required. 

Approximately 91% of poll replies were “TRUE”. There were no “DON’T KNOW” replies, so respondents seemed to know despite 9% disagreeing. There seem to be a lot of people who “KNOW BUT AREN’T TELLING”!

Table 1. Results of LinkedIn groups’ polls. “Impressions” count clicks on poll pages. 

GroupMTBFASQRRDIEEERelRelLeaBioStaTotal
Members87547303328246201798651539
TRUE0633539128
FALSE1614113
DON’T KNOW000000
TRUE%0%91%75%93%90%91%
FALSE%100%9%25%7%10%9%
Total votes16945710141
Proportion0.11%1.46%0.12%0.23%0.06%0.27%
“Impressions”40235012457653908669

The correct answer is FALSE! Lifetime data is available “hidden” or “masked” in counts: births and deaths, ships and returns, SARS, Ebola or COVID-19 infections, recoveries, or deaths, covered calls, breast implant and explants, etc.; even in HIV+->AIDS->death counts. 

Counts are statistically sufficient to make nonparametric estimates of MTBF, reliability, failure rate, or survivor functions, often with several failure modes simultaneously, censored or not. Often, ships and returns counts are implicit in revenue and cost data required by generally accepted accounting principles. Often, counts are population data, so estimates from them have no sample uncertainty. Furthermore, it is possible to quantify the uncertainty in MTBF, reliability, failure rate, and survival function estimates without life data.

In 1990 at Apple Computer, Mike Johanns regarded computer’s lives as service times in M/G/infinity self-service systems: M stands for Poisson inputs and G stands for the hidden distribution of times-to-failure. Mike told me to do a literature search for “Self-Service Queues” and “Statistics.” The only hit was my 1973 paper. The reliability estimation methods also work for nonstationary Poisson inputs M(t)/G/infinity systems [Nelson and Leemis].

Table 2. Ships and returns counts and least-squares reliability function estimate for a low-power mouse. Return counts in 3rd column are mixed from shipped cohorts in same month and previously.

MonthsShipsReturnsAgeReliability
Feb-87201829410.9832
Mar-875816930320.9832
Apr-878483352730.9764
May-87110037105640.9764
Jun-8795356132650.9764
Etc.    
Jun-89129501634280.9449
Figure 1. Low-Power Mouse reliability estimate circa 1990

See www.accendoreliability.com articles and examples. See https://sites.google.com/site/fieldreliability/ for snarky opening page and links to books and articles.

L. L. George and A. Agrawal, “Estimation of a Hidden Service Distribution of an M/G/Infinity Service System,” Naval Research Logistics Quarterly, vol. 20, pp. 549-555, 1973

B. L. Nelson and L. M. Leemis, “The Ease of Fitting but Futility of Testing a Nonstationary Poisson Processes from One Sample Path,” 2020 Winter Simulation Conference (WSC), Orlando, FL, USA, pp. 266-276, doi: 10.1109/WSC48552.2020.9383930, 2020

Filed Under: Articles, on Tools & Techniques, Progress in Field Reliability?

About Larry George

UCLA engineer and MBA, UC Berkeley Ph.D. in Industrial Engineering and Operations Research with minor in statistics. I taught for 11+ years, worked for Lawrence Livermore Lab for 11 years, and have worked in the real world solving problems ever since for anyone who asks. Employed by or contracted to Apple Computer, Applied Materials, Abbott Diagnostics, EPRI, Triad Systems (now http://www.epicor.com), and many others. Now working on survival analysis, epidemiology, and their applications: epidemics, randomized clinical trials, risk-based inspection, and DoE for risk equity.

« New Safety And Environmental Management System (SEMS) From MMS
The Role and Responsibilities of the Engineering Manager »

Leave a Reply Cancel reply

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

Articles by Larry George
in the Progress in Field Reliability? article 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