The Role of Statistics in Reliability Engineering

This Isn’t Your High School Statistics Course

What does the word statistics bring to mind when you hear it? Horrible high school classes that you sat through wondering why do I need this stuff? Complex math problems what were almost impossible to figure out? Or is it nonsense that is only used in political polls?

Statistics are used in every aspect of Reliability Engineering. So if you don’t have a good relationship with statistics, I highly recommend that you are starting investing time in building your knowledge in statistics. [Read more…]

by Fred Schenkelberg 2 Comments

The Sum of Squares Concept

The data analysis course professor tended to focus on the practical application of statistics.

Avoiding statistical theory was fine with me. Learning statistics for me was on how to solve problems, optimize designs, and understanding data.

Then one lecture started with the question, “Why do we sum squares regression analysis, ANOVA calculations, and with other statistical methods?” He paused waiting for one us to answer.

We didn’t. I feared the upcoming lecture would include arcane derivations and burdensome theoretical annotations. It didn’t. [Read more…]

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Math, Statistics, and Engineering

In college, Mechanics was a required class from the civil engineering department. This included differential equation.

Luckily for me, I also enjoyed a required course called analytical mechanics for my physics degree. This included using Lagrange and Hamiltonian equations to derived a wide range of formulas to solve mechanisms problems.

In the civil engineering course, the professor did the derivation as the course lectures, then expected us to use the right formula to solve a problem. He even gave us a ‘cheat sheet’ with an assortment of derived equations. We just had to identify which equation to use for a particular problem and ‘plug-and-chug’ or just work out the math. It was boring. [Read more…]

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Improve Decision Making with Statistics

We make decisions all the time. Often our decision making is with little more than a gut feeling.

When faced with a major decision we often look data to help us decide. Is the product reliable enough as designed? Which field returns indicate we should stop production?

Some decision may help us earn or lose thousands if not millions of dollars.

Deciding to delay a product launch by six months means we have no revenue for the duration. The delay may also permit us to address a design flaw that would cause half the products to fail within a few months.

The later may cause loss of market share, erosion of brand loyalty, not to mention the cost of warranty claims. [Read more…]

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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…]

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How to Encourage the Use of Statistics

If nothing was uncertain we would not need statistics.

Since nearly everything varies in some fashion, we need a way to describe and work with that variability.

We already know this and we know about statistics as being the right set of tools. Yet we hesitate, avoid, and refuse to pick up the appropriate tool. [Read more…]

by Fred Schenkelberg 2 Comments

Probability and Reliability Goals

Roll the dice.

It is about that simple if any one product will survive to a specific time. Every product has a chance, not a guarantee. The time to failure for each product is a function of the use, stresses, assembly, latent defects or imperfections, and many other variables.

The result is generally unknown. And, we often establish a reliability goal that includes the probability of success. Keep in mind that a probability is only meaningful when defined over a specific duration.

[Read more…]

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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…]