Accendo Reliability

Your Reliability Engineering Professional Development Site

Home » Articles » Page 123

Articles

Find all articles across all article series listed in reverse chronological order.

by 2 Comments

‘Making’ and ‘checking’ reliability are very, very different

‘Making’ and ‘checking’ reliability are very, very different

At the 2019 Annual Reliability and Maintainability Symposium, I was involved in a discussion with US Department of Defense (DoD) reliability engineering teams and industry representatives. And on the agenda was a review of an emerging document called the ‘Reliability and Maintainability Engineering Management Body of Knowledge.’ Let’s call this the ‘DoD RAM BOK’ for short.

I only had access to this document’s quick reference guide, and it suggests that the DoD RAM BOK describes what reliability engineering activities need to happen from ‘concept’ through to ‘operations’ for a typical military capability being introduced into service. 

[Read more…]

by Leave a Comment

Collecting Failure Data: A Practical Approach

How to collect the failure data needed to drive improved plant performance.

The first step to any reliability improvement program is to define what data or information will be required to drive defect elimination.  In the previous posts, the following topics were discussed;

All of the topics discussed what data is required and how to structure the data to implement a reliability engineering program or defect elimination process.   Understanding the what, when and why set up and is required to setup a reliability engineering program.  However, where many organization fail in this process is getting the data collected in a consistent and timely manner. [Read more…]

by Leave a Comment

What is your Risk Appetite?

What is your Risk Appetite?

Guest Post by John Ayers (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

Businesses take risks to pursue opportunities. What is your risk appetite? This question has been asked by companies for years. Risk appetite is the amount and type of risk an organization is able to support in the pursuit of business objectives. Examples of successful high risk appetite companies are: Amazon; Uber; and Tesla.

Risk appetite can be applied to your personal life as well especially today to thrive in changing times. Two stories of individuals with different risk appetite are presented below to illustrate how much you want to take can affect your life especially in retirement years. [Read more…]

by Leave a Comment

Planning for a DOE

Planning for a DOE

In this article post, we discuss the suggested steps for planning a Designed Experiment.

Step #1 – Clearly Define the Problem and Objectives

It is critical to clearly define the problem before beginning experimentation.  When the problem is not clearly defined and described, there will be confusion in designing and executing effective studies.  To define appropriate responses to measure requires that the problem be understood and agreed on.  Also, it is key to define the objectives of the actual experiment.  If the problem is to reduce scrap rate, how much of a reduction is targeted? [Read more…]

by Leave a Comment

Outsourcing or ‘Out of sight, out of mind?’

Outsourcing or ‘Out of sight, out of mind?’

I was having a lunch meeting with a project manager, and the topic of outsourcing came up. Specifically, outsourcing the manufacture of a particular machine that would form part of a larger vehicle system. I asked him why he was doing this, particularly when for many years the predecessor to this machine was very successfully manufactured in-house. His answer was simple.

It was to transfer risk to a supplier. [Read more…]

by Leave a Comment

Should You Worry About IoT If You Don’t Have the Basics Down?

What Good Is The Data If You Don’t Know What To Do With It?

As maintenance, reliability, and asset management professionals, we are in an amazing time.  We can collect virtually limitless amounts of data on the condition of our assets.  With this data, we can determine the exact condition of the assets, predict when the next failure is likely to occur and how it will occur.  Besides, with all of this data, we can move to prescriptive maintenance, where the maintenance actions are determined based on the asset condition, not a predetermined strategy.   I’ll touch more on prescriptive maintenance in next week’s post. [Read more…]

by Leave a Comment

Uptime Insights – 1 – Improvement Strategy

Uptime Insights – 1 – Improvement Strategy

Reliable operations are far less expensive to maintain and operate and they produce more consistently. Yet most industrial operations are far from achieving high reliability. Getting there will require effort and that effort goes well beyond the maintenance department alone. They will need to change from reactive, break it then fix it thinking and un-informed cost-cutting measures that undermine reliability. They will need leadership, not management. Leadership is all about making change and taking your organization in new directions.  It will be disruptive, or it won’t be much of a change. Leaders are the ones who rock the boat, managers will keep it stable.  In choosing excellence you’ll be choosing a path of constant change and improvement. Leadership is needed – it’s about strategy, effective execution, and it’s about your people – without whom you will accomplish very little.  If you want excellence, it begins with leadership. [Read more…]

by Leave a Comment

Why should you proactively identify Failure Modes?

Why should you proactively identify Failure Modes?

Identifying the right Failure Modes is an essential part of a Failure Modes and Effects Analysis (FMEA). One of the criteria for including a Failure Mode in an FMEA is if the Failure Mode is unlikely to occur, but has severe consequences. Doing so puts you in a position of strength to decide what, or if anything at all, should be done proactively to manage it. [Read more…]

by Leave a Comment

Nonparametric Forecasts From Left-Censored Data

Nonparametric Forecasts From Left-Censored Data

“Component D” had some failures in its first 12 months. How many more would fail in 36-month warranty? ASQ’s Quality Progress Statistics Roundtable published the data and Weibull analysis. The data included left-censored failure counts collected at one calendar time. The Weibull analysis included actuarial failure forecasts. This article describes nonparametric alternatives to Weibull and quantifies extrapolation uncertainty. The nonparametric forecasts are larger than the Weibull forecasts. Alternative extrapolations of nonparametric failure rates from data subsets quantify uncertainty. [Read more…]

by Leave a Comment

How Should the Sample Size be Selected for an X-bar Chart? (Part I)

How Should the Sample Size be Selected for an X-bar Chart? (Part I)

The purpose of control charts is to detect significant process changes when they occur.  In general, charts that display averages of data/measurements (X-bar charts) are more useful than charts of individual data points or measurements.  Charts of individuals are not nearly as sensitive as charts of averages at detecting process changes quickly.  X-bar charts are far superior at detecting process shifts in a timely manner, and the sample size is a crucial element in ensuring that appropriate chart signals are produced. [Read more…]

by Leave a Comment

The Informative Run Chart

The Informative Run Chart

One of the very first plots to do with a string of data is a simple run plot. This plot provides information related to location, trends, patterns, and anomalies, The plot of the data over time is a rather informative chart.

You have most likely constructed many run charts; if not you really should. This short introduction to the run chart will cover creating and interpreting them. Plus we’ll mention a few cautions and tips, as well. [Read more…]