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
    • 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
    • Asset Reliability @ Work
  • 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
      • 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
    • 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
    • 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 James Kovacevic Leave a Comment

What Can You Do With Data?

What Can You Do With Data?

A Question & Answer Period with Fred Schenkelberg and James Kovacevic on what can be done with your data and analysis.

Data and the analyses that use the data can be tricky to manage at best, let along extremely difficult.

In this last post of the series on using the maintenance data you have, Fred and James will answer many of the common questions asked about data and the analyses. [Read more…]

Filed Under: Articles, Maintenance and Reliability, on Maintenance Reliability Tagged With: data

by nomtbf Leave a Comment

Life Data Analysis with only 2 Failures

Life Data Analysis with only 2 Failures

Life Data Analysis with only 2 Failures

Here’s a common problem. You have been tasked to peer into the future to predict when the next failure will occur.

Predictions are tough.

One way to approach this problem is to do a little analysis of the history of failures of the commonest or system. The problem looms larger when you have only two observed failures from the population of systems in questions.

While you can fit a straight line to two failures and account for all the systems that operated without failure, it is not very satisfactory. It is at best a crude estimate.

Let’s not consider calculating MTBF. That would not provide useful information as regular reader already know. So what can you do given just two failures to create a meaningful estimate of future failures? Let’s explore a couple of options. [Read more…]

Filed Under: Engineering, MTBF Tagged With: data, life

by nomtbf Leave a Comment

A Life Data Analysis Challenge

A Life Data Analysis Challenge

old machinery couplingHere is a Challenge: Life Data Analysis

Some years ago a few colleagues compared notes on results of a Weibull analysis. Interesting we all started with the same data and got different results.

After a recent article on the many ways to accomplish data analysis, Larry mentioned that all one needs is shipments and returns to perform field data analysis.

This got me thinking: What are our common methods and sets of results when we perform life data analysis? [Read more…]

Filed Under: Engineering Tagged With: analysis, data, field data

by nomtbf Leave a Comment

The Many Ways of Data Analysis

The Many Ways of Data Analysis

Given Some Data, Do Data Analysis

Let’s say we have a set of numbers, {2.3, 4.2, 7.1, 7.6, 8.2, 8.4, 8.7, 8.9, 9.0, 9.1} and that is all we have at the moment.

How many ways could you analyze this set of numbers? We could plot it a few different ways, from a dot plot, stem-and-leaf plot, histogram, probability density plot, and probably a few other ways as well. We could calculate a few statistics about the dataset, such as mean, median, standard deviation, skewness, kurtosis, and so on. [Read more…]

Filed Under: Engineering Tagged With: data

by nomtbf Leave a Comment

How to Avoid Delivering Bad Data

How to Avoid Delivering Bad Data

14781654934_58be162f3b_zHow to Avoid Delivering Bad Data

We gather and report loads of data nearly every day.

Is your data “good data”? Or does it fall into the “bad data” category?

Let’s define the difference between good and bad data. Good data is accurate, timely, and useful. Bad data is not. It may be time to look at each set of data you are collecting or reviewing and judge if it’s good or not. Then set plans in motion to minimize the presence of bad data in your organization.

Good data is accurate

By this I mean it truly reprints the items or process being measured.

If the mass is 2.3 kilograms, then the measurement should be pretty close to 2.3 kg. This is a basic assumption we make when reviewing measurements, yet when was the last time you checked? Use a different measurement method, possible a known accurate method to check.

Measurement system analysis includes a few steps to determine if the gage making a measurement is true or not. Calibration may come to mind, as it is a step to verify the gage readings are reflecting standard measures. A meter is a meter is a meter across the many ways we can measure distance.

It also includes checking the common sources of measurement error:

  • Repeatability
  • Reproducibility
  • Bias
  • Linearity
  • Stability

You may also want to understand the resolution or discrimination of the measurement process.

If these terms and how one goes about checking for accuracy, it may be time to learn a little about MSA.

Good data is timely

If the experiment results are available a week after the decision to launch the product, it will not be considered in the decision. It is not useful for the decision concerning product launch. If the data was available it may alter the decision. Late, we will not know.

Timely means it is in time for someone or some team to make a decision. Ideally, the data is available immediately. When a product fails in the field, we would like to know right away, not two or three month later. If a production line becomes unstable, knowing before another unit of scrap is produced would be timely.

Not all data gathering and reporting is immediate. Some data takes months or an entire year to gather. There are physical constraints in some situation that day the gathering of data. For example is takes on average 13 minutes, 48 seconds, for radio signals to travel from a space probe orbiting Mars to reach Earth [1]. If you are making important measurements on Earth it should be a shorter delay.

The key point here, is the data should be available when it is needed to make decisions.

Good data is useful

Even if the data is accurate and timely is may not be useful. The data could be from a perfect measurement process, yet is measuring something we do not need to know or consider. The data gathered does not help inform us concerning the decision at hand.

For example, if I’m perfectly measuring production throughput, it does not help me understand the causes of the product line downtime. While related to some degrees, instead of the tally of units produced per hour, what we really would find useful is data concerning the number of interruptions to production, plus details on the root cause of each.

Setting up and maintaining the important measurements is difficult as we often shift focus based on the current data. We spot a trend and want to learn more than the current data can provide. The idea is we should not setup and only use a fixed set of data collection processes. Ideally your work to gather data is driven by the need to answer questions.

  • Is the maintenance process improving the equipment operation?
  • Is our manufacturing process stable and capable of creating our product?
  • Will the current product design meet life expectations/requirements?
  • Have we confirmed the new design ‘fixed’ the faults seen in the last prototype?

We have questions and we gather data to allow us to answer questions.

How would you describe the data you will look at today? Good or Bad? And more importantly, do you know if your data is good or bad?

—

Time delay between Mars and Earth, Thomas Ormhston, posted 5/8/2012,  European Space Agency, Mars Express Blog, http://blogs.esa.int/mex/2012/08/05/time-delay-between-mars-and-earth/ accessed 4/29/2016

Filed Under: Engineering Tagged With: data, measure

by Kirk Gray Leave a Comment

What will Advance Reliability Engineering?

What will Advance Reliability Engineering?

In all aspects of engineering we only make improvements and innovation in technology by building on previous knowledge. Yet in the field of reliability engineering (and in particular electronics assemblies and systems), sharing the knowledge about field failures of electronics hardware and the true root causes is extremely limited. Without the ability to share data and teach what we know about the real causes of “un-reliability” in the field, it is more easily understood why the belief in the ability able to model and predict the future of electronics life and MTBF continue to dominate the field of electronics reliability

[Read more…]

Filed Under: Accelerated Reliability, Articles, on Product Reliability Tagged With: analysis, data

by Fred Schenkelberg 9 Comments

Field Data and Reliability

Field Data and Reliability

Customers experience product failures.

Understanding these failures that occur in the hands of customers is an essential undertaking. We need this information to identify increasing failure rates, component batch or assembly errors, or design mistakes. [Read more…]

Filed Under: Articles, Musings on Reliability and Maintenance Topics, on Product Reliability Tagged With: data, field failure, field returns

by Fred Schenkelberg Leave a Comment

The Music of Data

The Music of Data

We are good at collecting data, now use it

In Katmandu, I visited a small pottery factory. There was a young man sitting at a potting wheel making candle stands. He didn’t measure anything and I doubt anyone did.

Based on experience and just looking at a finished item, he could tell if it was acceptable or not. That was good enough.

[Read more…]

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

by Fred Schenkelberg Leave a Comment

What will Advance Reliability Engineering?

What will Advance Reliability Engineering?

Kirk Gray, Accelerated Reliability Solutions, L.L.C.

In all aspects of engineering we only make improvements and innovation in technology by building on previous knowledge. Yet in the field of reliability engineering (and in particular electronics assemblies and systems), sharing the knowledge about field failures of electronics hardware and the true root causes is extremely limited. Without the ability to share data and teach what we know about the real causes of “un-reliability” in the field, it is more easily understood why the belief in the ability able to model and predict the future of electronics life and MTBF continue to dominate the field of electronics reliability [Read more…]

Filed Under: Engineering Tagged With: analysis, data, reliability, validity

The Accendo Reliablity logo of a sun face in circuit

Please login to have full access.




Lost Password? Click here to have it emailed to you.

Not already a member? It's free and takes only a moment to create an account with your email only.

Join

Your membership brings you all these free resources:

  • Live, monthly reliability webinars & recordings
  • eBooks: Finding Value and Reliability Maturity
  • How To articles & insights
  • Podcasts & additional information within podcast show notes
  • Podcast suggestion box to send us a question or topic for a future episode
  • Course (some with a fee)
  • Largest reliability events calendar
  • Course on a range of topics - coming soon
  • Master reliability classes - coming soon
  • Basic tutorial articles - coming soon
  • With more in the works just for members
Speaking of Reliability podcast logo

Subscribe and enjoy every episode

RSS
iTunes
Stitcher

Join Accendo

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

It’s free and only takes a minute.

Join Today

Dare to Know podcast logo

Subscribe and enjoy every episode

RSS
iTunes
Stitcher

Join Accendo

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

It’s free and only takes a minute.

Join Today

Accendo Reliability Webinar Series podcast logo

Subscribe and enjoy every episode

RSS
iTunes
Stitcher

Join Accendo

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

It’s free and only takes a minute.

Join Today

Recent Articles

  • Why Fist to Five is a Powerful Decision-Making Technique
  • Project Requirements are a Risk
  • The Role and Responsibilities of the Engineering Manager
  • Poll: “Is life data required…?”
  • New Safety And Environmental Management System (SEMS) From MMS

© 2023 FMS Reliability · Privacy Policy · Terms of Service · Cookies Policy

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.