This allows you to evaluate the expected lifetime of your product and address areas of concern ahead of time – saving you both real and reputational costs. Plus, those analyses can be performed during the design phase of your system before actual manufacturing and deployment. One of advantages of using MTBF as a measure for reliability assessment is that there are widely used and accepted methods of calculating it. MTBF is a helpful metric because it enables you to assess the average lifetime of your product or system. One single iteration of that product may be an outlier from the MTBF, but on a large scale, the lifetime of most of your products will correlate to the MTBF value. An MTBF will let you gauge the lifetime of the average product that you produce. Some customers rated it poorly, some rated it great, and on average, it was rated 4 stars. Take a look on Amazon at any product with four stars and you will see this exact trend. But chances are, from the rating shown on Amazon, it will be deserving of four stars. Or it might last a lifetime and deserve all 5 stars. It might break quickly and deserve a two-star rating from you. If one product has two stars and another product has four stars, which one are you going to buy? We bet the one with four stars is already in your shopping cart.ĭoes that four-star rating mean that the single product you receive will be worthy of four stars? Absolutely not. One of the most important data points that many of us use when deciding whether to purchase an item on Amazon is the rating stars that previous customers have given the product. Our favorite analogy for MTBF is the rating stars on Amazon – because we all shop on Amazon! MTBF and an Analogy with the Amazon Rating System Chances are you manufacture hundreds or thousands of circuit boards, and each circuit board will fail at a different time. With all of those data points, you will start to be able to calculate an average. Therefore, it becomes very difficult to determine how long one circuit board will function without failing.įortunately, it’s very unlikely that you only manufacture one circuit board. Some examples include the quality level of the components you procured, manufacturing variability, shipping problems, customers’ misuse, etc. There are many variables which can affect how well your circuit boards perform. Or, the time to failure might fall somewhere in between. If you build one circuit board and sell it, that single circuit board might fail very quickly. Let’s say you manufacture circuit boards. If you have an attention to detail, like many reliability engineers do, you may have noticed one very key word in the definition of MTBF: average. ![]() Read on and fear not, MTBF is still helpful! MTBF as an Average That seems like an unexpected result, doesn’t it? This quick example might make you start to wonder what exactly is MTBF and how can you use it. So, if you have a product with an MTBF of 100 hours, you only have a 36.79% chance that it actually functions for 100 hours! This will indicate the probability that a system with an MTBF of 100 hours will still be functioning after 100 hours of operation. To make it interesting, let’s also calculate reliability at 100 hours. Let’s convert our previous MTBF value of 100 hours to reliability as an example. t is the end time, in hours, that you are interested in.e is the mathematical constant approximately equal to 2.71828.The key difference is that MTBF is the amount of time between failures and reliability is the probability that the system is still functioning at a certain time.Įven though MTBF and reliability are different, you can very easily convert MTBF to reliability by using this equation for exponential distributions: You can probably already start to see the difference between MTBF and reliability. For example, a reliability of 0.8 at 100 hours indicates that after 100 hours, there is an 80% chance that the system is still functioning. Reliability is defined as the probability that a system will perform its intended function for a specified period of time. ![]() For example, an MTBF of 100 hours indicates that a system, on average, will successfully operate for 100 hours before experiencing a failure. MTBF, or Mean Time Between Failures, is the average amount of time between failures of a system. Let’s start with some definitions so we can see the difference between MTBF and reliability. If MTBF isn’t the same as reliability, what exactly is it? Is there a relationship between MTBF and reliability? And more importantly, why is MTBF helpful if it isn’t the same as reliability? The short answer is no, which may surprise you. This brings up a common question: is MTBF the same as reliability? ![]() One of the most well-known ways of estimating product reliability is to calculate its MTBF, or Mean Time Between Failures.
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