Your Reliability Engineering Professional Development Site
Chris and Fred discuss whether reliability engineers need to learn statistics—and why the real value lies in understanding what the numbers actually mean. They explain how statistics can clarify decisions, prevent costly mistakes, and cut through bad analysis.
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Big decisions require months or years to make, which makes communication associated with them a long game. The FINESSE fishbone diagram provides the seven essential elements for effective communication for big decisions. Playing on the theme of a fish, a fish’s top fin provides it with direction. Let’s explore the top fin of the FINESSE fishbone diagram.
[Read more…]Chris and Fred discuss the ability to get ‘data sets’ to practice reliability analysis on! … sounds like a party …
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Here’s a common problem: You have been tasked with peering into the future to predict when the next failure will occur.
Predictions are tough.
One way to approach this problem is to analyze the history of failures of the most typical system. The issue looms larger when you have only two observed failures from the population of systems in question.
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 readers 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…]
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…]
In this video, Hemant Urdhwareshe explains how to use Hazard Plotting to estimate parameters of Weibull Distribution using Excel with an application case study. Hemant also explains related mathematical concepts in Hazard plotting. Hemant is a Fellow of ASQ, and is certified by ASQ as CRE, CMBB, CSSBB, CQE and CMQ/OE. Viewers may like to watch our previous video on Weibull Probability Plotting.
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A parametric Life Analysis involves “forcing” or “imposing” a distribution’s parameters on a data set in order to obtain the “best fit”. However, it can lead to errors in results. The non-parametric estimation suggests that there are other approaches though not necessarily the easiest or “most elegant” ones. In the field of reliability engineering, we tend to like something so much that we use them in every “sauce”. A classic example is the Weibull Distribution. It has become so popular that Life Analysis is also known as a “Weibull Analysis”. As a reminder, the Weibull distribution is only one parametric distribution amongst a myriad of others, invented by Walodi Weibull in 1937. Dr Bob Abernathy’s New Weibull Handbook1 quotes: “the Weibull distribution provides reasonably accurate failure and failure forecasts……”. Thus, parametric distributions are good enough but not perfect to make a decision.
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With the kind permission of Wayne Nelson and Robert Abernathy, we are posting an article on the analysis of repair data. As you may know, the assumptions made when using simple time-to-failure analysis of repairable systems may provide misleading results. Using the analysis method outlined by Wayne is one way to avoid those costly mistakes.
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In this video, Hemant Urdhwareshe discusses how to analyse complete data of failures to identify distribution, identify distribution parameters, estimate reliability at a specified time using Minitab (version 19) software. The file used in the video can be downloaded here: Reliability Data.xls.zip.
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In this video, you will learn how to perform statistical calculations: mean and standard deviation on Casio fx991-MS Calculator.
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Determining which parts of stock can be a very overwhelming process. As such, many choose to blindly accept the OEM or Manufacturer’s recommendations. And why shouldn’t they? The OEM has many years of experience in building these types of assets and supplying spares, right?
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Software tools are a cornerstone of modern Reliability Engineering, enabling reliability practitioners to perform their analysis without getting bogged down in the details of the underlying mathematical processes. There are many software tools available for reliability engineering, some of which are tailored to this application, while others are more general statistical tools which can be adapted to the needs of reliability engineers. One thing these tools have in common is their graphical user interface (GUI). The GUI requires only a basic level of knowledge to operate, but with a few clicks of the correct buttons, the desired task can be achieved with relatively little mental effort. It is the user friendly GUI that draws reliability engineers to select such applications as their tools of choice for performing reliability engineering analyses.
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…]
RCM Implementations don’t fail, believe me if you understand what your getting into upfront, use a proven process to select your asset for analysis AND you commit the resources, your implementation will be a huge success.
Looking back 20 years I can clearly remember performing my first RCM analysis and the ordeal that followed as we struggled to:
a) See the value in the analysis we had just completed (Someone told us that RCM should be performed on every piece of equipment at our plant so we selected one of our most common assets.
b) Free up the resources necessary to implement the tasks that came out of the analysis. [Read more…]
Analyzing and visualizing data that is related to equipment wear-out (fatigue stage) over a specific time span is a challenge. Analyzing systems, equipment and components exposed to spectrum loading as opposed to uniform cyclical loading that span years or decades or centuries requires a special approach. Think of roads, bridges and other assets with long life spans that are susceptible to wear out mechanisms caused by external and internal loads, varying load durations, temperature swings and corrosion. [Read more…]
