# Reliability is about making the right decision, each time.

A common role during a first assignment as a reliability engineering is to answer a question or accomplish a task. It may help someone to make reliability decisions.

We may be asked, as I was, how long will this new product function during use? The director of engineering wanted to know if the new design was reliable enough to meet the customer’s requirements concerning reliability. He didn’t ask it that way, yet he did have a question that needed answering.

Sometimes we run a batch of tests, conduct failure analysis on field returns, or compare the durability to two vendor subsystems. In each case, there is a question to be answered.

A decision to be made by someone else. Continue reading “Enabling Great Reliability Decisions”

# Field Failure: A Quality or Reliability Problem

When my car fails to start, as a customer I only know that my car didn’t start.

When my phone fails to turn on, or the dishwasher leaks, or the printer jams, I only know I’ve experienced an unwanted outcome.

I really do not care, at the moment, why the coffee maker is not producing my morning cup of coffee. My first thought is ‘now where do I find a cup of coffee?’ As a reliability engineer I’m naturally curious about what caused the failure and can I fix it immediately to get the morning cup brewing.

My thinking does not classify the failure or the source of the failure as a quality or reliability problem. Then why is it that some organizations split reported field failures thus? Continue reading “Field Failure: A Quality or Reliability Problem”

# Reliability is Not Metrics, It’s Decision Making

MTBF, KPIs, yield, return rate, warranty… bah!

We may use one or more of these when establishing product reliability goals. When tracking performance. When making decisions.

Goals, objectives, specifications, and requirements, are stand-ins for the customer’s experience with the product.

We’re not trying to reduce warranty expenses or shouldn’t be solely focused on just that measure. We need to focus on making decisions that allow our product deliver the expected reliability performance to the customer. Continue reading “Reliability is Not Metrics, It’s Decision Making”

# 5 Ways Your Reliability Metrics and Fooling You

We measure results. We measure profit, shipments, and reliability.

The measures or metrics help us determine if we’re meeting out goals if something bad or good is happening, if we need to alter our course.

We rely on metrics to guide our business decisions.

Sometimes, our metrics obscure, confuse or distort the very signals we’re trying to comprehend.

Here are five metric based mistakes I’ve seen in various organizations. Being aware of the limitations or faults with these examples may help you improve the metrics you use on a day to day basis. I don’t always have a better option for your particular situation, yet using a metric that helps you make poor decisions, generally isn’t acceptable.

# The Variety of Statistical Tools to Support Your Decision Making

My wife and I moved to a new home last year. We have yet to organize our tools.

The bedroom and kitchen are now organized. We, for the most part, can find the sweater or pan that we’re seeking.

No so for our tools in the shop. We have an assortment of hand tools for painting, home maintenance, yard work, and woodworking. In our previous home, we had the tools on pegboards, on shelves, in cabinets. We could find the right tool for the job at hand quickly. We’ve avoided the tool aisle at the hardware store recently, as we were sure we had the tool we need in the jumbled mess in our garage already. Still haven’t found it, though.

Have you noticed the number of statistical tools available? It’s like visiting a well-stocked tool store. There are basic tools like trend charting and advanced tools like proportional hazard models. Let’s explore the available tools a little so you can quickly find the right tool for the question or problem you are facing today. Continue reading “The Variety of Statistical Tools”

# Two people have shaped how I guess an answer.

Their comments and guidance have tailored how to form a quick estimate, my ability to articulate a hunch and the effectiveness of those guesses.

You probably guess or make a rough estimate regularly. How good is your gut feel? Do you keep track and score yourself?

Making an estimate should be second nature for you. It’s not something to do in public, too often. The practice can aid you in numerous ways.

## Physics, Calculations, and Estimates

In my first physics class in college, we regularly ‘enjoyed’ pop quizzes. One, in particular, provided a lesson that has stuck with me for my entire life.

Here is the quiz question (as far as I recall it’s wording)

“How many piano tuners are there in New York City? 3 minutes, show your work.”

The homework for the class from the previous class included calculating acceleration given force on mass or something like that. Not a hint that we would be tested on census values.

There were a few groans across the room. A few pencils started to scratch out some answer.

How would you answer? What work would you show? Keep in mind I went to college a long time ago, and we did not enjoy the benefits of a Google search or anything even remotely similar. I had not lived in NYC nor played piano.

The lecture after the quiz discussed the value of making a reasonable estimate or educated guess before performing the experiment or calculation. Physics involves math and just knowing the formulas does not guarantee you will get the right answer.

He likely mentioned a quote, or I ran across it later, by John Archibald Wheeler attributed to his book, Spacetime Physics.

Never make a calculation until you know the answer. Make an estimate before every calculation, try a simple physical argument (symmetry! invariance! conservation!) before every derivation, guess the answer to every paradox and puzzle. Courage: No one else needs to know what the guess is. Therefore make it quickly, by instinct. A right guess reinforces this instinct. A wrong guess brings the refreshment of surprise. In either case life as a spacetime expert, however long, is more fun! – John Archibald Wheeler, Spacetime Physics

This process helped me throughout my career. More than once helping me catch a missing sign that altered a calculated result. When I would guess the result of a calculation should show an increasing speed, and my calculation shows the falling rock has a decreasing speed over time, I would find the dropped negative sign and correct my calculation.

Another exercise we did in class was to sum a set of numbers, quickly. The first step was to jot down a guess, an order of magnitude or rough estimate. If the list is ten positive three-digit numbers, the result is going to be greater than 1,000. How much more, roughly? If the results of you addition work is -237, could that possibly be right?

Just because Excel churns out a number doesn’t mean it’s right. How do you know or at least how do you check?

## Improve the Effectiveness of Your “Back of the Envelope” Estimates

I’d been making these educated guesses regularly for years. Then I shared an estimated value of a proposed project with Helen.

Helen had the office next to mine at HP at the time. She is an inventive and wicked smart engineer. I often shared ideas and proposals with her as her insights and advice always improved my work.

The proposal was exciting for me as I expected $10 million in value for a rather modest investment of time. She stopped me right there. She said, “$10 million, really? I don’t believe that.” Or something to the effect.

Continuing the discussion, I quickly went over the assumptions and back of the envelope calculations that supported the claim.

She didn’t question the logic nor the actual results. Rather she wondered if the nice round numbers I used could be slightly altered. Sure, they only rough estimates, such as 100,000 customers or $1,000 in the cost of each failure, etc. She said when she hears a nice round number she instantly knows it is a guess or estimate. Her guard goes up as she becomes distracted by the round number rather than focusing on the logic and assumptions. Helen recommended I alter the final result to include three significant digits. Instead of$10 million, how about $9.87 million. It’s about the same as the result I got using a sequence of round numbers, yet Helen suggested it was “a bit more believable.” Hum. Never thought of that. I always focused on the assumptions and logic, not the result. I thought the order of magnitude was sufficient to convey the result. As you know, everyone filters what they hear and accept. Recognizing some recoil when they hear a nice round number meant I could lessen the effects of that kind of filter by simply using a three digit estimate. So, I tried it. Over the next year each time I presented a guest or estimate I alternated between nice round numbers and estimates with three significant digits. The round number estimates always generated questions about the values and numbers that went into the result. The three significant digit estimate often were not questioned or enjoy questions about the assumptions and logic only. I kept track that year and twice as many proposals using the three significant digit rule moved forward. You will have to make quick estimates, work out a rough return on investment, or forecast return on investment values. In these cases you need to make a guess, then using the set of assumptions and a bit logic refine your estimate in just a short time (never having all the data you need). When presenting the results try using three significant digits. Record these estimates and remind yourself to check how well it turns out if possible. Also, note how your audience responds to the ‘believable’ estimate you present. ## Your Next Estimate How many will view, share, and respond to this article? Please, right now, without too much thought, add your guesses to the comment section below. A month after this article posts I’ll tally up the shares and post the actual answer. I think I can find counts for views and the comment count will suffice for the last element. ## Teaching Reliability is Part of Your Role # Teaching Reliability is Part of Your Role Nearly everyone I’ve ever met doesn’t like their toaster to fail. It will, and that is a bummer, as the quick and easy way to warm up the morning toast will be thwarted. Failures happen. As reliability engineers, we know that failures happen. Helping others to identify potential failures, to avoid failures or to minimize failures is what we do best. It is out ability to teach others about reliability engineering that allows us to be successful. Continue reading “Teaching Reliability is Part of Your Role” ## Math, Statistics, and Engineering # 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. Continue reading “Math, Statistics, and Engineering” ## When Do Failures Count? # When Do Failures Count? One technique to calculate a product’s MTBF is to count the number of failures and divide into the tally of operating time. You already know, kind reader, that using MTBF has its own perils, yet it is done. We do not have to look very far to see someone estimating or calculating MTBF, as if it was a useful representation of reliability… alas, I digress. Counting failures would appear to be an easy task. It apparently is not. Continue reading “When Do Failures Count?” ## The Relationship Between Reliability Goals and Confidence # Reliability Goal and Confidence We establish reliability goals and measure reliability performance. They are not the same thing. Goals and measures, while related, are not the same nor serve the same purpose. Continue reading “The Relationship Between Reliability Goals and Confidence” ## Bought a House Due to Pokemon Go # Walking, Playing and Bought a House Seriously, while out walking, listening to a podcast, and playing Pokemon Go, found an open house to view. A week later our offer was accepted and next week we close. I would not have been out walking that Sunday afternoon if not out playing Pokemon Go. Glad there are no dangerous cliffs nearby. Continue reading “Bought a House Due to Pokemon Go” ## 3 Ways to Improve your Reliability Program # A Few Simple Ideas to Improve Your Reliability Program Spending too much on reliability and not getting the results you expect? Just getting started and not sure where to focus your reliability program? Or, just looking for ways to improve your program? There is not one way to build an effective reliability program. The variations in industries, expectations, technology, and the many constraints, shape each program. Here are three suggestions you can apply to any program at any time. These are not quick fix solutions, nor will you see immediate results, yet each will significantly improve your reliability program and help you achieve the results you and your customers expect. Continue reading “3 Ways to Improve your Reliability Program” ## What is Reliability? ### Guest Post by Martin Shaw In today’s complex product environment becoming more and more electronic, do the designers and manufacturers really understand what IS Reliability ?? It is NOT simply following standards to test in RD to focus only on Design Robustness as there is too much risk in prediction confidence, it only deals with the ‘intrinsic’ failure period and rarely has sufficient Test Strength to stimulate failures. Continue reading “What is Reliability?” ## Failure Happens – It Is What Happens Next That Matters # Failure Happens – It Is What Happens Next That Matters One of the benefits of reliability engineering is failure happens. Nothing made, manufactured, or assembled will not fail at some point. It is our desire to have items last long enough that keeps us working. Since failures happen, our work includes dealing with the failure. ## Not My Fault Years ago while preparing samples for life testing at my bench, I heard an ‘eep’ or a startled sound from a fellow engineer. It was quickly followed by an electrical pop noise and a plum of smoke. Something on the circuit board she was exploring had failed. With a pop and smoke. She didn’t move. At this point, my initial amused response turned to concern for her safety. She was fine, just startled as the failure was unexpected. She quickly claimed it wasn’t her fault. It was her design, she selected and assembled the parts, and she was testing the circuit. Yet, it wasn’t her fault. She did not expect a failure to occur (a blown capacitor – which we later discovered was exposed to far too much voltage), thus it was not her fault. We hear similar responses from suppliers of components. It must have been something in your design or environment that caused the failure, as the failure described shouldn’t happen. It’s not expected. Well, guess what, it did happen. Now let’s sort out what happened and not immediate assign blame for who’s fault it is. The ‘not my fault’ response so a failure is not helpful. Failures are sometimes the result of a simple error and quickly remedied. Other are complex and difficult to unravel. The quicker we focus on solving the mystery of the cause of the failure, the quicker we can move on to making improvements. ## Warranty With possibly too many ‘not my fault’ responses, laws now enjoin the manufacture of products to stand behind their product. If a failure occurs, sometimes within specific conditions, the customer may ask for a remedy from the supplier. If failures did not happen there would be no such thing as a warranty. A warranty is actually a legal obligation, yet has turned into a marketing tool. A long warranty implies the product is reliable and by offering a long warranty the manufacturer is stating they are shifting the risk of failure to themselves. A repair or replacement is generally not adequate recompense for a failure, yet it provides some restitution. In most cases, it only provides peace of mind, if the item doesn’t fail. The warranty business has become an industry in of itself. Selling, servicing, and honoring warranties is something that others can deal with outside your organization. The downside is the lack of feedback about failure details so you can affect improvements. A manufacturer shouldn’t hide behind their warranty policy, nor ignore the warranty claim details. It is one-way a customer can voice their expectations concerning product reliability. You should listen. ## Repair services My favorite outsourced repair service story involved a misguided payment structure. If you pay a repairman based on the value of the components replaced, they will likely always replace the most expensive components. If the repair is accomplished by resetting a loose connector, nothing is replaced, and the repairman is not compensated for the diagnostic work and effective repair. If he instead immediately replaced the main circuit board, and in the process reseating most of the connectors, the repair is fast, effective, and he is handsomely rewarded. See the problem? When a failure occurs, it may be natural to offer a repair service as the remedy. It should be quick (not a two-week wait as with my local cable company to restoring a fallen line), and efficient for all parties involved. For the owner of the equipment, we want the functionality restored as quickly as possible and cost effectively as possible. For the manufacture of the equipment, we want cost effectiveness, plus the knowledge concerning the failure. Does your repair service provide for the needs of both parties as well as the repair technician? ## Fail safe Sometimes when a failure occurs nothing happens. We might not even notice the failure occurs. Other times the product simply goes ‘cold’ or a function is lost. Nothing adverse, no pop or smoke, occurs. We call this failing safe. It’s more complicated than my simple explanation, yet it is the desired repose to a failure. The product itself should not create more damage, cause harm, place someone in peril. It should fail safely and preferably quietly. If the ignition falls from the ignition switch, which may be considered a failure to retain the key within the switch, the driver should not lose control of the vehicle. This is in part a safety feature, yet is also a common expectation that the failure of a system should not create other problems. Failure containment is related. How does your product fail? Safely? ## Maintenance For some failures, such as the degradation of lubricants, we perform maintenance. When the brake pads or tire tread wears to marginally safe level we replace the brake pad or tire. If we can anticipate the failure pattern we perform preventive maintenance. Creating a maintainable piece of equipment is one response to failures. It allows creating complex equipment with failure prone elements. Through maintenance, we are able to restore the system to operation or avoid unexpected downtime. If failures didn’t occur, we wouldn’t need maintenance. We have some control over the nature of the maintenance activities. For some types of failures, we can only execute corrective maintenance. For others, we can use preventative methods. The idea is to anticipate and avoid the widest range of failures through effective maintenance practices, that remains cost effective. Adding maintenance practices in response to system failures is not the duty of the owner of the equipment. It is a design function to anticipate the system failures that may occur and devise the appropriate maintenance plan to thwart unwanted failures from occurring. The two parties actually have to work together to make this work well. ## Expectations When I buy a product, I know that some proportion of products like the one I just purchased will failure prematurely. I just do not want or desire mine to fail. My expectation is the one I select at the store is a good one. It won’t let me down, stranded, or injured. That is my expectation. When a failure does occur and I value the functionality the product provides I will want to restore the unit via repair or replacement, sometimes via a service contract or warranty or repair center. To a large degree, my expectation is after a failure all will go well. As the manufacture of products, when a failure occurs, your expectations may include learning from the failure to make improvements. Or it should. We know we cannot anticipant nor avoid every failure that may occur. The expectation on both sides is to make robust and dependable products that provide value for all involved. When that approach fails, we fail. ## Failure Happens In response to a failure, it’s how the product, customer, and manufacture responds that matters. A simple failure can turn into a disaster for all involved. Or the failure can provide insights leading to breakthrough innovations and new opportunities. It’s how we respond that matters. How do you respond to failures? ## Are the Measures Failure Rate and Probability of Failure Different? # Are the Measures Failure Rate and Probability of Failure Different? Failure rate and probability are similar. They are slightly different, too. One of the problems with reliability engineering is so many terms and concepts are not commonly understood. Reliability, for example, is commonly defined as dependable, trustworthy, as in you can count on him to bring the bagels. Whereas, reliability engineers define reliability as the probability of successful operation/function within in a specific environment over a defined duration. The same for failure rate and probability of failure. We often have specific data-driven or business-related goals behind the terms. Others do not. If we do not state over which time period either term applies, that is left to the imagination of the listener. Which is rarely good. ## Failure Rate Definition There at least two failure rates that we may encounter: the instantaneous failure rate and the average failure rate. The trouble starts when you ask for and are asked about an item’s failure rate. Which failure rate are you both talking about? The instantaneous failure rate is also known as the hazard rate h(t) ￼￼￼￼$latex \displaystyle&s=3 h\left( t \right)=\frac{f\left( t \right)}{R\left( t \right)}$Where f(t) is the probability density function and R(t) is the relaibilit function with is one minus the cumulative distribution function. The hazard rate, failure rate, or instantaneous failure rate is the failures per unit time when the time interval is very small at some point in time, t. Thus, if a unit is operating for a year, this calculation would provide the chance of failure in the next instant of time. This is not useful for the calculation of the number of failures over that year, only the chance of a failure in the next moment. The probability density function provides the fraction failure over an interval of time. As with a count of failures per month, a histogram of the count of failure per month would roughly describe a PDF, or f(t). The curve described for each point in time traces the value of the individual points in time instantaneous failure rate. Sometimes, we are interested in the average failure rate, AFR. Where the AFR over a time interval, t1 to t2, is found by integrating the instantaneous failure rate over the interval and divide by t2 – t1. When we set t1 to 0, we have ￼$latex \displaystyle&s=3 AFR\left( T \right)=\frac{H\left( T \right)}{T}=\frac{-\ln R\left( T \right)}{T}\$

Where H(T) is the integral of the hazard rate, h(t) from time zero to time T,
T is the time of interest which define a time period from zero to T,
And, R(T) is the reliability function or probability of successful operation from time zero to T.

A very common understanding of the rate of failure is the calculation of the count of failures over some time period divided by the number of hours of operation. This results in the fraction expected to fail on average per hour. I’m not sure which definition of failure rate above this fits, and yet find this is how most think of failure rate.

If we have 1,000 resistors that each operate for 1,000 hours, and then a failure occurs, we have 1 / (1,000 x 1,000 ) = 0.000001 failures per hour.

Let’s save the discussion about the many ways to report failure rates, AFR (two methods, at least), FIT, PPM/K, etc.

## Probability of Failure Definition

I thought the definition of failure rate would be straightforward until I went looking for a definition. It is with trepidation that I start this section on the probability of failure definition.

To my surprise it is actually rather simple, the common definition both in common use and mathematically are the same. There are two equivalent ways to phrase the definition:

1. The probability or chance that a unit drawn at random from the population will fail by time t.
2. The proportion or fraction of all units in the population that fail by time t.

We can talk about individual items or all of them concerning the probability of failure. If we have a 1 in 100 chance of failure over a year, then that means we have about a 1% chance that the unit we’re using will fail before the end of the year. Or it means if we have 100 units placed into operation, we would expect one of them to fail by the end of the year.

The probability of failure for a segment of time is defined by the cumulative distribution function or CDF.

## When to Use Failure Rate or Probability of Failure

This depends on the situation. Are you talking about the chance to failure in the next instant or the chance of failing over a time interval? Use failure rate for the former, and probability of failure for the latter.

In either case, be clear with your audience which definition (and assumptions) you are using. If you know of other failure rate or probability of failure definition, or if you know of a great way to keep all these definitions clearly sorted, please leave a comment below.