Your Reliability Engineering Professional Development Site
All articles listed in reverse chronological order.
Finished it. 130 pages long and packed with advice on why and how to switch from MTBF to reliability.
Based in large part on comments, feedback, discussions and input from you, my peers in the NoMTBF tribe. Thanks for the encouragement and support.
Similar to the Weibull distribution yet with slightly heavier tails. While not as easy to interpret if the data shows early life or wear out features, the lognormal distribution often fits time to repair data accurately.
Transform the data by taking the natural log of each data point. The resulting values tend to be normally distributed if the original data fits a lognormal distribution.
You can use base 10 or base 2 or any base and the results will still tend to be normally distributed. It is common to use natural log, ln(). [Read more…]
Worst-case tolerance analysis is the starting point when creating a tolerance specification.
It is a conservative approach as it only considers the maximum or minimum values of part variation—whichever leads to the worst situation. Setting tolerances such that the system will function given the expected variation of manufactured components improves that ability of the system to perform reliably.
In the worst-case method, you simply add the dimensions using the extreme values for those dimensions. Thus, if a part is specified at 25 ± 0.1 mm, then use either 25.1 or 24.9 mm, whichever leads to the most unfavorable situation.
The actual range of variation should be the measured values from a stable process. It may be based on vendor claims for process variation, industry standards, or engineering judgment. [Read more…]
A discreet distribution useful when counting events within a time period.
It models rates, such as the number of gophers in your garden, paint scratches on your car, or the number of shopping carts that arrive in the 5 minutes before you in line. Essentially it’s the count of something over a time period or defined area. [Read more…]
First off, not sure why anyone would want to do this, yet one of the issues I’ve heard concerning abandoning the use of MTBF is client ask for MTBF. If they will not accept reliability probabilities at specific durations, and insist on using MTBF, you probably should provide a value to them.
Let’s say you have a Weibull distribution model that described the time to failure distribution of your product. You’ve done the testing, modeling, and many field data analysis and know for the requestor’s application this is the best estimate of reliability performance. You can, quite easily calculate the MTBF value.
As you know, if theβ parameter is equal to one then the characteristic life, η, is equal to MTBF. If β is less than or greater than one, then use the following formula to determine the mean value, MTBF, for the distribution.
$latex \displaystyle&s=4 \mu =\eta \Gamma \left( 1+\frac{1}{\beta } \right)$
You’ll need the Gamma function and the Weibull parameters. The further β is from one, the bigger the difference between η and MTBF.
You can find a little more information and background at the article Calculate the Mean and Variance on the accendoreliability.com site under the CRE Preparation article series.
The exponential distribution is a model for items with a constant failure rate (which very rarely occurs).
If the chance of failure is the same each hour (or cycle, etc.), including the first hour, 100th hour, and 1 millionth hour or use, then the exponential distribution is suitable.
Many practitioners assume the failure rate either is constant or changes so little as to be essential constant in order to make reliability calculations simpler. [Read more…]
And, being encouraged to write a paper or two on drone reliability. Now that Amazon has a delivery drone patent, and industrial applications continue to announced daily, there is a need for serious reliability in these devices.
The early adopters and explorers in any nascent industry generally discover the many design faults including reliability issues. That is common.
As the drone industry develops, improving product reliability becomes a business necessity. For industrial application it is essential. [Read more…]
It is not enough to simply state your organization has a proactive stance concerning reliability. It more than running a few tests or thinking about reliability before the product ships or the equipment is installed.
It is a way of doing business.
After a short convulsion of disbelieve I became shocked. This was a guide for a government agency advising design teams and supporting reliability professional. It suggested using 217 to create the estimate of field reliability performance during the development phase.
Have we made not progress in 30 years?
Let’s say you are reviewing a purchase contract and find a request for a reliability estimate based on Mil Hdbk 217F (the latest revision that is also been obsolete for many years), what would you do? Would you contact the author and request an update to the document? Would you pull to 217 and create the estimate? Would you work to create and estimate the reliability of a product using the best available current methods? Then convert that work to an MTBF and adjust the 217 inputs to create a similar result. Or would you ignore the 217 requirement and provide a reliability case instead?
When a customer demands a parts count prediction as a condition of the purchase, is that useful for either the development team or the customer?
No.
So, given the contract is signed and we are in the execution phase, what are your options?
Ask the customer to adjust the agreement to include a meaningful estimate.
Ignore the 217 requirement and provide a complete reliability case detailing the reliability performance of the product.
Find a new position that will not include MTBF parts count prediction.
I hope you would call out the misstep in the contract and help all parties get the information concerning reliability that they can actually use to make meaningful decisions.
A point estimate of the mean of a population is determined by calculating the mean of a sample drawn from the population. The calculation of the mean is the sum of all sample values divided by the number of values. [Read more…]
Asking the right question is important.
During a review meeting (informal or formal) asking a few reliability questions may reveal weaknesses, strengths, or uncertainty. The design team has many priorities and reliability is often difficult to estimate, yet knowing what is and isn’t known provides a clear picture of risks for decision makers.
If you are a decision maker and need to ascertain the reliability risks of the current design, then asking a couple of questions may provide just the insights you need. It also conveys that reliability is on your mind and that you want to have answers that are meaningful and well thought out.
MTBF is the inverse of a failure rate, it is not reliability. Physics of failure (PoF) is a fundamental understanding and modeling of failure mechanisms. It’s the chemistry or physical activity that leads a functional product to fail. PoF is also not reliability.
Both MTBF and PoF have the capability to estimate or describe the time to failure behavior for a product. MTBF requires the knowledge of the underlying distribution of the data. PoF requires the use stresses and duration to allow a calculation of the expected probability of success over time.
MTBF start with a point estimate. PoF starts with the relationship of stress on the deterioration or damage to the material. One starts with time to failure data and consolidates into a single value, the other starts with determining the failure mechanism model.
Given the ability to model at the failure mechanism level even for a complex system, is there a need to summarize the time to failure information into a single value?
No.
MTBF was convenient when we had limited computing power and little understanding of failure mechanisms. Today, we can use the time to failure distributions directly. We can accommodate different stresses, different use pattern and thousands of potential failure mechanisms on a laptop computer.
MTBF has no purpose anymore. MTBF describes something we have and should have little interest in knowing.
Sure, PoF modeling takes time and resources to create. Sure, we may need complex mathematical models to adequately describe a failure mechanism. And, we may need to use simulation tools to estimate time to failure across a range of use and environmental conditions. Yet, it provide an estimate of reliability that is not possible using MTBF at any point in the process. PoF provides a means to support design and production decisions, to accommodate the changing nature of failure rates given specific experiences.
When will we stop using MTBF? I think the answer to both is about the same time. It is going to happen when we, reliability minded professionals, decide to use the best available methods to create information that support the many decisions we have to make. PoF will become dominant soon. It provides superior information and superior decision, thus superior products. The market will eventually decide, and everyone will have to follow. Or, we can decide now to provide our customers reliable products.
We can help PoF become dominant by not waiting for it to become dominant.
The basic measures of central tendency are mean, median, and mode.
Given a collection of data, a common question is about where the data resides. Knowing the center or mid-point or average is a starting point as we consider the data. [Read more…]
Selecting a supplier for components or subsystems involves many aspects including the desired reliability performance.
Once selected the ability of the supplier to provide items that meet or exceed the reliability requirements relies on their understanding of the requirements and operational conditions related to the specific item within the system. It also relies on the supplier’s knowledge of their own design and manufacturing processes as it related to the reliability performance. [Read more…]
A troublesome question arrived via email the other day. The author wanted to know if I knew how and could help them adjust the parameters of a parts count prediction such that they arrived at the customer’s required MTBF value.
I was blunt with my response. [Read more…]