Your Reliability Engineering Professional Development Site
All articles listed in reverse chronological order.
Is it possible for an individual to ‘do’ DFR? Is design for reliability something, like a specific technique, that is DFR?
What is DFR and how would you recognize it if it was occurring? Like meditation, nearly anyone can strike a pose that appears similar to someone in deep meditation, yet can you tell by observation if they really are mediating? Probably not. The same is true for an organization or person that declares they are doing DFR. Maybe they are or maybe not.
I will not say ‘never’, which is probably what you expect. There are a rare set of circumstances which may benefit with the use of MTBF as a metric. Of course, this does not include being deceitful or misleading with marketing materials. There may actually be an occasion where the MTBF metric works well.
As you know, MTBF is often estimated by tallying up the total hours of operation of a set of devices or systems and dividing by the number of failures. If no failures occur we assume one failure to avoid dividing by zero (messy business dividing by zero and to be avoided). MTBF is essentially the average time to failure.
The metric we select should be measurable and of a measure we have an interest. We would like to detect changes, measure progress, and possibly make business decisions with our metrics. If we are interested in the expected value of the time to failure for our devices, then MTBF might just be useful.
When making a device we often hear of executives, engineers and customers talk about how long they expect the product to last. An office device may have an expected life of 5 years, a solar power system – 30 years, and so on. If by duration we all agree that we expect 5 years of service on average, then using the average as the metric makes sense.
Before starting the use of MTBF, just make sure that a 5 year life implies half or two thirds of the devices will fail by the stated duration of 5 years. Yes, if the time to failure distribution is actually described by the exponential distribution (and a few other distributions) it means that two third of the units are expected to failure by the MTBF value. Thus if we set the goal to 5 years MTBF we imply half or more of the units will fail by 5 years.
Having a goal helps the design and development team make decisions and eventually conduct testing to prove the design meets the reliability objectives. Setting the goal a the expected value allows the fewest number of samples for testing. Testing for 99% reliability over 5 years is much tougher. We may require many samples to determine a meaningful estimate of the leading tail (i.e. first 1% or 5% of failures) of the time to failure distribution.
If the time failure pattern fits an exponential distribution, then testing becomes simplified. We can test one unit for a long time, or many units a short time, and arrive at the same answer. The test planning can maximize our resources to efficiently prove our design meets the objective. When the chance of failure each hour is the same, every device-hour of testing provide an equal amount of information.
Unlike products that wear out or degrade with time, when the design and device exhibit an exponential distribution we do not need any aging studies. We can just apply use or accelerated stress and measure the hours of operation and count the failures. Also any early failures are obviously quality issues and most likely do not count toward failures that represent actual field failures. Or do they?
When the industry, organization, vendors, and engineering staff already use MTBF to discuss reliability, then management would be wise to establish a metric using MTBF. Makes sense, right? The formula to calculate MTBF is very simple. Even the name implies the meaning (no pun intended). MTBF is the mean time between (or before) failure. It’s an average, which calculators, spreadsheets, smart phones, and possibly even your watch can calculate.
While the spread of the data is often of importance when making comparisons, estimating a sample set of data’s confidence bounds, or estimating the number of failures over the warranty period, if we assume the data actually fits an exponential distribution, we find the mean equals the standard deviation. Great! One less calculation. We have what we need to move forward.
Nearly every reliability or quality textbook or guideline includes extensive discussions about MTBF the exponential distribution and a wide range of reliability related calculations. Our common understanding generally is supported by the plentiful references.
Ask a few folks around you when considering using MTBF. What do they define MTBF as representing? If you receive a consistent answer, you may just have a common understanding. If the understanding is also aligned with the underlying math and assumptions, even better.
In summary all you need is:
I submit we are rarely interested in the time till the bulk of devices fail, rather interested in the time to first failures or some small percentage fail
I suggest that very few devices or system actually fail with a constant hazard rate. If your product does, prove it without grand waves of assumptions.
I have found that engineers, scientists, vendors, customers, and manager regularly misunderstand MTBF and how to properly use an MTBF value.
So back to the opening statement, it is possible though not likely you will find an occasion to effectively use MTBF as a metric. Instead use reliability: the probability of successful operation over a stated period with stated conditions and definition of success. 98% of office printers will function for 5 years without failure in a office…. Pretty clear. Sure we can fully define the function(s) and environment, and we need to do that anyway.
In English there is a lot of confusion on what reliability, availability and other ‘ilities mean in a technical way. Reliability as used in advertising and common discussions often means dependable or trustworthy. If talking about a product or system it may mean it will work as expected. [Read more…]
Environmental testing is the evaluation of a product or system in one or more stress conditions. Environmental as in that which surrounds and affects a product. Consider temperature. Is the product going to experience outdoor temperatures as found in Fargo, North Dakota or Belmopan, Belize?
The weather is one way to describe external stresses, yet it is so much more. Environmental testing may include fungus, insect, and animal exposure. The document MIL-STD-810G lists and describes testing methods for a wide range of environmental conditions. [Read more…]
On social media the other day ran across a comment from someone that took my breath away. They were looking forward to starting a new reliability, no, MTBF report. They were tasked with creating a measure of reliability for use across the company and they choose MTBF.
Sigh.
I certainly do not blame the person. They have read about MTBF in many textbooks. Studied reliability using MTBF and related measures, plus found technical papers using the same. They may have seen industry reports and standards also.
MTBF is prevalent and no wonder someone tasked with setting a metric would select MTBF. It’s easy to calculate. Just one number and bigger is better.
MTBF is roundly criticized across any reliability related forum or discussion group. There is progress in books, papers and standards. And, it’s not reaching those new to reliability engineering.
This note will be short and have one request. Please tell those just getting started in reliability engineering to please not consider using MTBF. To not request MTBF from vendors. And, to actually do some thinking before selecting MTBF as their organizations metric.
Better yet, challenge those using MTBF to explain in a coherent and rational manner why they are doing so. Ask them to validate their assumed constant failure rate or similar assumptions. Working together we can start a ripple that may help build the wave of knowledge to improve the state of reliability engineering.
Before computers and statistical software, we relied on tables to determine values for common integration problems – the normal distribution in particular. There is no closed form solution for the integral of the normal distribution probability density function, it requires advanced numerical methods to estimate the area under the curve. [Read more…]
Stop testing when the testing provides no value.
If no one is going to review the results or use the information to make a decision, those are good signs that the testing provides no value. Of course, this may be difficult to recognize.
Some time ago while working with a product development team, one of the tasks assigned was to create an ongoing reliability test plan. This was just prior to the final milestone before starting production. During development, we learned quite a bit about the product design, supply chain, and manufacturing process. Each of which included a few salient risks to reliable performance.
In an ideal world the design of a product or system will have perfect knowledge of all the risks and failure mechanisms. The design then is built perfectly without any errors or unexpected variation and will simply function as expected for the customer.
Wouldn’t that be nice.
The assumption that we have perfect knowledge is the kicker though, along with perfect manufacturing and materials. We often do not know enough about:
We do know that we do not know everything we need to create a perfect product, thus we conduct experiments.
We test. [Read more…]
There are two basic ways to consider the central limit theorem. First consider a random variable, X, which has a mean, μ, and variance σ2. If we take a random sample from f(X) of size n and calculate the sample mean, X̄, then as n increases the distribution of the sample means, X̄’s approaches a normal distribution with mean, μ, and variance σ2/√n̄. The original data, X, may have any distribution and when n is suitably large the distribution of the averages will approach a normal distribution. [Read more…]
In the Monte Carlo method, one uses the idea that not all parts have the same dimensions, yet a normal distribution describing the variation of the part dimensions is not assumed.
Although the normal distribution does commonly apply, if the process includes sorting or regular adjustments or if the distribution is either clipped or skewed then the normal distribution may not be the best way to summarize the data.
As with any tolerance setting, getting it right is key for the proper functioning of a product. Monte Carlo method allows you to consider and use the appropriate models for the variations that will exist across your components. [Read more…]
Let’s say we 6 months of life data on 100 units. We’re charged with looking at the data and determine the impact of fixing the problems that caused the earliest failures.
The initial look of the data includes 9 failures and 91 suspensions. Other then the nine all units operated for 180 days. The MTBF is about 24k days. Having heard about Weibull plotting and using the beta value as a guide initially find the blue line in the plot. The beta value is less than one so we start looking for supply chain, manufacturing or installation caused failures, as we suspect early life failures dominant the time to failure pattern.
Given clues and evidence that some of the products failed early we investigate and find evidence of damage to units during installation. In fact it appears the first four failures were due to installation damage. The fix will cost some money, so the director of engineer asks for an estimate of the effect of the change on the reliability of the system.
The organization uses MTBF as does the customer. The existing MTBF of 24k days exceeds the customers requirement of 10K days, yet avoiding early problems may be worth the customer good will. The motivation is driven by continuous improvement and not out of necessity or customer complaints.
One way to estimate the effect of a removal of a failure mechanism is to examine the data without counting the removed failure mechanism. So, if the change to the installation practice in the best case completely prevents the initial four failures observed we are left with just the 5 other failures that occurred over the 6 months.
Removing the four initial failures and calculating MTBF we estimate MTBF will change to about 300 days.
Hum?
We removed failures and the MTBF got worse?
The classic calculation for MTBF is the total time divided by the number of failures. Taking a closer look at time to failure behavior of the two different failure mechanisms may reveal what is happening. The early failures have a decreasing failure rate (Weibull beta parameter less than 1) over the first two months of operation. Later, in the last couple of months of operation, 5 failures occur and they appear to have an increasing rate of failure (Weibull beta parameter greater than 1).
By removing the four early failures the Weibull distribution fit changes from the blue line to the black line (steeper slope).
Recall that the MTBF value represents the point in time when about 63% of units have failed. With only 9 total failures out of 100 units we have only about 10% of units failed so the MTBF calculation is a projects to the future when most of have failed, it does not providing information about failures at 6 months or less directly.
In this case when the four early failures are removed the slope changed from about 0.7 to about 5, it rotated counter clockwise on the CDF plot.
If only using MTBF the results of removing four failures from the data made the measured MTBF much worse and would have prevented us from improving the product. By fitting the data to a Weibull distribution we learned to investigate early life failures, plus once that failure mechanism was removed revealed a potentially serious wear out failure mechanism.
This is an artificial example, of course, yet it illustrates the degree which an organization is blind to what is actually occurring by using only MTBF. Treat the data well and use multiple methods to understand the time to failure pattern.
A continuous distribution is useful in many statistical applications such as process capability, control charts, and confidence intervals about point estimates. On occasion time to failure, data may exhibit behavior that a normal distribution models well. [Read more…]
The root sum squared (RSS) method is a statistical tolerance analysis method. In many cases, the actual individual part dimensions fall near the center of the tolerance range with very few parts with actual dimensions near the tolerance limits.
This, of course, assumes the part dimensions are tightly grouped and within the tolerance range.
Setting tolerances well, using the best available data about the part(s) variation, allows creating designs that function well given the expected part variation. This is better for reliable performance. Also, the same method can be applied when the loads and stresses are normally distributed.
Check that assumption with you data first, of course. [Read more…]
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