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.
[Read more…]Articles tagged Data analysis
Using Casio FX 991 MS Calculator for Statistical Calculations
In this video, you will learn how to perform statistical calculations: mean and standard deviation on Casio fx991-MS Calculator.
[Read more…]Using Reliability Analysis to Determine Spares Stocking
How to use an FMECA or RCM Analysis to Determine What Spares to Stock
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?
[Read more…]
Reliability Engineering using Python
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.
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…]
The Recipe for RCM Success!
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…]
Using rain-flow counting methods for process wear out studies
Guest Post Prepared by Eugene Danneman, Wind Wear LLC
Introduction
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…]
Making Your Reliability Data Analysis Count
Ensuring Reliability Data Analysis Leads to Positive Action
Convince, don’t confuse! Justify, don’t exaggerate!
Project managers want to deliver their product on time and on schedule. Design engineers want to believe that they have got it right. But your analysis, test results and field data suggest that there might be a problem. What do you do?
The key words here are “suggest” and “might be”. How should you present your evidence and analysis such that it doesn’t exaggerate with certainty, or confuse with statistics? How should you ensure that your conclusions lead to positive action? [Read more…]
The Next Step in Your Data Analysis
Nothing keeps a statistician happy like a pile of data.
Part 6 of 7
As seen in the previous articles, you can easily use the data you already have to conduct a meaningful analysis. This includes Weibull, Crow-AMSAA or a Mean Cumulative Function analysis.
Digging into a well manage dataset promises to reveal insights, trends, and patterns that will help improve the line, process, or plant.
Creating a plot or calculating summaries is pretty easy with today’s tools. Yet, are you doing the right analysis or are the various assumptions valid?
One critical step in the data analysis process is making sure you are doing a valid and appropriate analysis. [Read more…]
First Step in Analyzing Repairable Systems Data
Using the right plot enables your team to know what is working or need improvement.
Part 4 of 7
Your facility has data and maybe too much data. Using simple plotting may be the key to unlocking how well your maintenance program is performing.
Building on the concept of reliability growth modeling James Kovacevic described a convenient way to quickly visualize your repairable system failure data is with a mean cumulative function (MCF) plot. [Read more…]
Show me the Data
Early in my career, I worked for an unreasonable person.
He wanted us, his engineering staff, to show him the data. He wanted us to gather, monitor, analyze and display data regularly. Anytime we needed approval, funding, or resources he wanted to see the data. [Read more…]
Two Birds with One Stone
Just back from a trip to Patagonia and catching up with emails and writing this morning. Posting an article for this list is due today along with a touch of travel weariness, decided to share a part of a question received concerning data analysis.
My thought is to post an actual question one of our peers is facing, and meet the deadline for this post. [Read more…]
Sources of Reliability Data
We rely on data to make decisions, to reveal patterns or trends, to learn about our systems and world. Data has many forms and sources. Reliability data may provide what will fail and/or when a device will fail. [Read more…]
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.
Just Plot The Data
One of the first things taught in a data analysis class, or in first-grade math, is the plot. A graphical representation of the data. Bar charts, pie charts, histograms, box plots, and the x-y scatter plot. These and others simply help us to understand the nature of the data.
The ‘nature’? The data is only a record of an observation. Counts, colors, numbers, or something similar. The ‘nature’ is, to me, the behavior, maybe pattern, or story the data may reveal.