data analysis Archives - Accendo Reliability

Process Capability Analysis I – Overview and Indices

Introduction

How is a manufacturing process determined to be capable of producing parts that meet engineering requirements? Some, like the finish of a gear tooth are critical, while the roughness of a non-contact surface isn’t critical. The critical characteristics need to be identified and checked to determine if the process is capable.

This article defines the analysis concepts and indices.

Prototypes and Series Production

Process capability assessments (PCA) start during the product development process and continue into series production. Prototype parts are created by a variety of methods, ranging from hand-crafted parts to short term manufacturing in a production intent process under factory conditions. The methods that are closest to series production are the most realistic.

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by Fred Schenkelberg Leave a Comment

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…]

by Les Warrington 2 Comments

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…]

by Fred Schenkelberg 2 Comments

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…]

by Fred Schenkelberg Leave a Comment

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…]

by Fred Schenkelberg 5 Comments

Field Data Analysis First Look

Field data analysis starts with the collection of data.

In a previous article, we used a Nevada chart to gather the counts per month of field failure data. The chart also provides the necessary data to account for how many units have not failed as of yet.

The Nevada chart on its own is just a table of numbers and does not reveal patterns of the changing nature of failure rates over time. Are we experiencing early life failures or wear-out related failures?

We need to conduct some data analysis to learn what message the data contains. [Read more…]

by Fred Schenkelberg Leave a Comment

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.

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by Fred Schenkelberg Leave a Comment

Variance

In another post, I started the discussion about variability and interquartile range. This is part 2 of that discussion and will focus on variance.

With rare exception, most distributions or groups of data require more than one parameter (or statistic) to fully describe both the location and spread (scale and shape) of the group of data. Is the data clumped tightly about some value, or spread out over a wide range. [Read more…]