Blog - Accendo Reliability

First Impressions

Note: This first article in the NoMTBF campaign was published on April 1st, 2009. Thus, we’ve been at this and making progress for a long time and come a long was since starting the NoMTBF campaign. I am looking forward to your comments, contributions, and suggestions.
Fred

At first, MTBF seems like a commonly used and valuable measure of reliability. Trained as a statistician and understanding the use of the expected value that MTBF represented, I thought, ‘Cool, this is useful.’

Then, the discussions with engineers, technical sales folks, and other professionals about reliability using MTBF started. And the awareness that not everyone, and at times it seems very few, truly understood MTBF and how to properly use the measure.

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by Fred Schenkelberg Leave a Comment

2015 Recommended References Survey Results

This is the first annual survey to find what you recommend for those preparing for the ASQ CRE exam.

See the full list of reliability references for the CRE exam, for reliability and maintenance engineers at Accendo Reliability. [Read more…]

by Laxman Pangeni Leave a Comment

The Hidden Power of the P-Diagram in Engineering Design

Introduction

When we think of reliability tools, the FMEA often takes the spotlight. But if you’re skipping the P-Diagram, you’re likely missing the very foundation of failure prevention.

In my 15+ years working on field reliability and failure analysis, I’ve come to view the Parameter Diagram (P-Diagram) as the unsung hero of robust design.

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by Michael Keer Leave a Comment

4: Design & Planning – Part 1: Design

by Mike Freier

In the next chapter in our series on taking a hardware product from idea to scale, we move into the Design & Planning stage. Minimal Viable Products (MVP) and roadmaps will help you identify the key features for your new product.

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by Semion Gengrinovich Leave a Comment

Why Weibull is so popular?

The Weibull distribution’s popularity across various industries stems from its remarkable flexibility and adaptability in modeling a wide range of data types, especially in reliability engineering and failure time analysis. This versatility is primarily due to the Weibull distribution’s shape parameter, which allows it to model different failure rates over time, aligning closely with the real-world behavior of many systems and components.

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by Fred Schenkelberg Leave a Comment

How to Judge a Reliability Book

By it’s cover no doubt. The title and cover are important, this is true. When you judge a reliability book we often first see and evaluate the cover.

The author? Do you buy the book based on who wrote or edited it?

Do you have a quick scan or check for key features before you add the book to your library? I’m curious how you select a book to use a reference for your work. The books we read and use for work shape our work, thus it’s important to have the right works at our disposal. [Read more…]

by Mike Sondalini Leave a Comment

Deciding When to Do a Reliability Failure Study

WHEN YOU DO A RELIABILITY FAILURE STUDY LOOKING TO PROTECT YOUR BUSINESS AGAINST LARGE UNCOMMON FAILURES, AND THOSE FAILURES THAT ARE VERY COMMON BUT THOUGHT TO BE TOO SMALL TO BE A SERIOUS PROBLEM, BE SURE TO DO LIFETIME FINANCIAL ANALYSIS WHEN DECIDING THE FAILURES TO INVESTIGATE

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I am to do a reliability failure study and would like to know the definition of repetitive failure with respect to some recognized reliability standard.
We want to apply the recommendations in the standard for failure codes in our CMMS system to decide which equipment failures need a reliability failure study. For example, we have a centrifugal pump bearing fail twice in a year. I am of the opinion this is a repetitive failure.

[Read more…]

by Greg Hutchins Leave a Comment

Tech Eats Companies

By Greg Hutchins (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

Over the next 10 years, I expect many more industries to be disrupted by software, with new world beating Silicon Valley companies doing the disruption in more cases than not.
Marc Andreessen – Venture Capitalist

We’ve been involved in engineering and tech automation for years. What surprises us is the observable breadth, depth, and impacts of automation (AI included) over the last five years, which COVID accelerated. What’s the big deal about automation?

[Read more…]

by Larry George 1 Comment

Uncertainty and Resource Allocation in the URC

This article appeared in 1983 in the ORSA Applied Probability SIG under the pseudonym “Anonymous”

In another country far away, there were power plants that generated cheap electricity by unclear means. Since operation of the plants involved some risk, the Unclear Regulatory Commission was established to license power plants for operations. The URC decreed that any plant could have a license if its probability of an unclear accident was smaller than 0.000001 per year. The plant operators said they were uncertain.

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by Nancy Regan Leave a Comment

Is Your Greatest Reliability Asset Underutilized?

In today’s short video, I share how the delivery of our new metal building reminded me of a key aspect of Reliability Centered Maintenance (RCM). RCM was designed to be carried out with a Facilitated Working Group approach, bringing together a team of equipment experts to answer the RCM questions.

By using this approach, not only do you get comprehensive answers, but you also capture and memorialize the knowledge and experience of your experts—something that could amount to over 100 cumulative years’ experience in the room!

Even if one of your team members retires or moves on, their knowledge remains. Watch to find out how you can make the most of this valuable resource.

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by Hemant Urdhwareshe Leave a Comment

B10 Life for Weibull and Lognormal Distributions

Dear friends, we are happy to upload this video on how to estimate B10 life when failure data follows Weibull or Lognormal Distribution. Your feedback on the video is welcome!

This video from the Institute of Quality and Reliability explains how to determine BX life for products that follow Weibull and log-normal distributions. BX life is defined as the time by which X percent of items are expected to fail. For example, B10 life means that 10% of items are expected to fail, which implies a 90% reliability.

We recommend watching following videos before watching this video for better learning experience:

Weibull Distribution Part-1

B10 Life for Exponential Distribution

Normal Distribution and Z-Score

Lognormal Distribution

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by Michael Keer Leave a Comment

3: Concept & Feasibility Phase – Part 2: Feasibility

by Mike Freier

The feasibility phase addresses two questions. First, can your design be made into a manufacturable product? And, second, how will you sell your product and to whom?

Validating the feasibility of manufacturing a new product is the next chapter in our series by Mike Freier on how to take a new hardware product from idea to scale.

[Read more…]

by Semion Gengrinovich Leave a Comment

Continuous Improvement

Continuous Improvement in product development is a systematic and ongoing approach to enhancing products processes and services. This concept is crucial for organizations to stay competitive, meet evolving customer needs, and maintain product quality.

Let’s explore the key concepts of continuous Improvement in product development. Continuous Improvement is an iterative process tha 2involves constantly evaluating and refining products and processes. It’s not a one-time effort but a cyclical approach that allows for ongoing enhancements based on feedback.

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by Fred Schenkelberg Leave a Comment

Should One Profit From Failures?

“Do not improve reliability as it cuts into our repair activity profits.” Is this a way to run a reliability program?

I’ve seen this in action and that company is no longer in business. In another situation the field service department withheld vital information to improve products lest his department (and self-importance) dwindle.

Is this a bad business model, or is it just my thinking it not so smart? [Read more…]

by Greg Hutchins Leave a Comment

When Should a Company Self-Disrupt

By Greg Hutchins (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

So, when should a company self-disrupt? Or, more importantly when should you self-disrupt.

Companies face these disruptor questions:

What are possible triggers to start (your) self-disruption?
1. When should they (you) self-disrupt?
  1. How quickly should they (you) self-disrupt?
    1. How should they (you) self-disrupt?
      1. What’s their (your) final destination?
        How are they (you) going to monetize (make money)?
        All companies are searching for the next killer work model, app, platform, product, or idea. It’s too late when there are not enough products in the pipeline to create a sustainable competitive advantage. Companies learned this lesson the hard way.

[Read more…]

by Mike Sondalini Leave a Comment

The Role of Chance and Luck in Your Equipment Failures

Plant and equipment do not fail by accident. There are causes. Whether equipment fails depends on the capacity of their parts to handle stresses, when stresses are applied and the size of those stresses. These are probabilistic events – they are random, with many possibilities. Timing the start of failure, or its continuation to a breakdown, is mostly speculation because it depends on which past and future scenarios occur. It seems that luck and chance has a large say on the lifetime reliability of equipment. But there is a way to guide equipment reliability and performance toward the results you want.

[Read more…]

by Larry George 1 Comment

Kaplan-Meier Reliability: What Could Possibly Go Wrong?

SAS, JMP, R-”Survival”, Minitab, ReliaSoft, XLStat, and perhaps other statistics programs offer the Kaplan-Meier nonparametric reliability estimator as a default. Take credit for using nonparametric reliability estimation and avoiding unwarranted assumptions. What could go wrong using the Kaplan-Meier estimator?

Cohorts could be non-stationary, random processes!
Failures could be recurrent process counts, not dead-forever!
Lifetime data depends on the censoring process(es); e.g., competing risks!
Greenwood’s variance estimator errs! Covariances are missing!
Alternative estimators could be more efficient than Kaplan-Meier!

Are you using all the information in data available from population data required by GAAP? If you don’t have lifetime data, use periodic failure counts. This article describes an example where the Kaplan-Meier estimator from grouped lifetime data is less efficient than using periodic failure counts, even though you don’t know which cohort they came from!

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