Category Archives: MTBF

Mean Time Between Failures or MTBF is a common metric for reliability and is often misused or misunderstood.

What is the MTBF Means?

What is the MTBF Means?

Guest post by Msc Teofilo Cortizo

The term MTBF (Mean Time Between Failures) within maintenance management, it is the most important KPI after Physical Availability. Unlike MTTF (Mean Time To Failure), which relates directly to available equipment time, MTBF also adds up the time spent inside a repair. That is, it starts its count from a certain failure and only stops its counter when this fault was remedied, started and repeated itself again. According to ISO 12849: 2013, this indicator can only be used for repairable equipment, and MTTF is the equivalent of non-repairable equipment.

The graphic below illustrates these occurrences:

Figure 01: Mean Time Between Failures
Figure 01: Mean Time Between Failures

Calculating the MTBF in the Figure 01, we have added the times T1 and T2 and divided by two. That is, the average of all times between one failure and another and its return is calculated. It is, therefore, a simple arithmetical calculation. But what does it mean?

Generally speaking, this indicator is associated with a reliability quality of assets or asset systems, and may even reach a repairable item, although it is rarer to have data available to that detail. Maintenance managers set some benchmark numbers and track performance on a chart over time. In general, the higher the MTBF the better, or fewer times of breaks and repairs over the analyzed period.

Once we have fixed the concepts, some particularities need to be answered:

1. Can we establish periodicity of a maintenance plan based on MTBF time?

2. Can I calculate my failure rates based on my MTBF?

3. Can I calculate my probability of failure based on my MTBF?

4. If the MTBF of my asset or system is 200 hours, after that time will it fail?

It is interesting to answer these questions separately:

1. Can we establish periodicity of a maintenance plan based on MTBF time?

The MTBF is an average number calculated from a set of values. That is, these values can be grouped into a histogram to generate a data distribution where the average value is its MTBF, or the average of the data. Imagine that this distribution follows the Gaussian law and we have a Normal curve that was modeled based on the failure data. The chart below shows that the MTBF is positioned in the middle of the chart.

Figure 02: Normal Distribution Model
Figure 02: Normal Distribution Model

In a modeled PDF curve (Probability Density of Failure) the mean value, or the MTBF, will occur after 50% of the failure frequencies have occurred. If we implement the preventive plan with a frequency equal to the MTBF time, it will already have a 50% probability of failing. Therefore, the MTBF is not a number that indicates the optimal time for a scheduled intervention.

 2. Can I calculate my failure rates based on my MTBF?

Considering the modeling of the failure data to calculate the MTBF, it´s only possible in the exponential distribution fix a value where the failure rate is the inverse of the MTBF:

MTBF = 1 / ʎ

In this distribution, the MTBF time already corresponds to 63.2% probability of failure.

Figure 03: Exponential Distribution Model
Figure 03: Exponential Distribution Model

Any modeling other than exponential, the failure rate will be variable and time dependent, so its calculation will also depend on factors such as the probability density function f(t) and the reliability function R(t).

ʎ(t) = h(t) = f(t) / R(t)

Although the exponential distribution is the most adopted in reliability projects, which would generate a constant failure rate over time, most of the assets have variations within their “bathtub curve”, as exemplified by Moubray:

Figure 04: Different Bathtub Curves (Moubray, 1997)
Figure 04: Different Bathtub Curves (Moubray, 1997)

 

This means that the exponential expression is not best suited to reflect the behavior of most assets in an industrial plant.

3. Can I calculate my probability of failure based on my MTBF?

As seen above, only in the exponential distribution has a constant failure rate that can be calculated as the inverse of the MTBF. In this case, yes, we can calculate the probability of failure of an asset using the formula below:

f(t) = ʎˑexp(-ʎt)

For other models where the failure rate depends on the time, it is only possible to calculate the probability of failure through a data modeling and determination of a parametric statistical curve.

4. If the MTBF of my asset or system is 200 hours, after that time will it fail?

The question is, what exactly does that number mean? It was shown that MTBF isn´t used as a maintenance plan frequency. According to the items explained above, this time means nothing as it is not comparable to its history over the months. If the parametric model governing the behavior of the assets in a reliability study is not determined, the time of 200 hours has no meaning for a probability of failure. In the case of the MTBF provided by equipment manufacturers is different, through life tests they determine exponential curves and thus calculate the time in which there will be 63.2% of sample failures.

I hope the article has helped us to reflect on the definitions of an indicator that is both used but also so misunderstood within industrial maintenance management.

Msc Teofilo Cortizo

Reliability Engineer

 

Consider the Decision Making First

Consider the Decision Making First

Reliability activities serve one purpose, to support better decision making.

That is all it does. Reliability work may reveal design weaknesses, which we can decide to address. Reliability work may estimate the longevity of a device, allowing decisions when compared to objectives for reliability.

Creating a report that no one reads is not the purpose of reliability. Running a test or analysis to simply ‘do reliability’ is not helpful to anyone. Anything with MTBF involved … well, you know how I feel about that. Continue reading Consider the Decision Making First

The Fear of Reliability

The Fear of Reliability

MTBF is a symptom of a bigger problem. It is possibly a lack of interest in reliability. Which I doubt is the case. Or it is a bit of fear of reliability.

Many shy away from the statistics involved. Some simply do not want to know the currently unknown. It could be the fear of potential bad news that the design isn’t reliable enough. Some do not care to know about problems that will requiring solving.

What ever the source of the uneasiness, you may know one or more coworkers that would rather not deal with reliability in any direct manner. Continue reading The Fear of Reliability

Being In The Flat Part of the Curve

What Does Being In The Flat Part of the Curve Mean?

To mean it means very little, as it rarely occurs. Products fail for a wide range of reasons and each failure follows it’s own path to failure.

As you may understand, some failures tend to occur early, some later. Some we call early life failures, out-of-box failures, etc. Some we deem end of life or wear out failures. There are a few that are truly random in nature, just as a drop or accident causing an overstress fracture, for example. Continue reading Being In The Flat Part of the Curve

A Series of Unfortunate MTBF Assumptions

A Series of Unfortunate MTBF Assumptions

The calculation of MTBF results in a larger number if we make a series of MTBF assumptions. We just need more time in the operating hours and fewer failures in the count of failures.

While we really want to understand the reliability performance of field units, we often make a series of small assumptions that impact the accuracy of MTBF estimates.

Here are just a few of these MTBF assumptions that I’ve seen and in some cases nearly all of them with one team. Reliability data has useful information is we gather and treat it well.  Continue reading A Series of Unfortunate MTBF Assumptions

Time to Update the Reliability Metric Book

It is Time to Update the Reliability Metric Book with Your Help

Let’s think of this as a crowdsourced project. The first version of this book is a compilation of NoMTBF.com articles. It lays out why we do not want to use MTBF and what to do instead (to some extent).

With your input of success stories, how to make progress using better metrics, and input of examples, stories, case studies, etc. the next version of the book will be much better and much more practical. Continue reading Time to Update the Reliability Metric Book

We Need to Try Harder to Avoid MTBF

We Need to Try Harder to Avoid MTBF

Just back from the Reliability and Maintainability Symposium and not happy. While there are signs, a proudly worn button, regular mentions of progress and support, we still talk about reliability using MTBF too often. We need to avoid MTBF actively, no, I mean  aggressively.

Let’s get the message out there concerning the folly of using MTBF as a surrogate to discuss reliability. We need to work relentlessly to avoid MTBF in all occasions.

Teaching reliability statistics does not require the teaching of MTBF.

Describing product reliability performance does not benefit by using MTBF.

Creating reliability predictions that create MTBF values doesn’t make sense in most if not all cases. Continue reading We Need to Try Harder to Avoid MTBF

3 Ways to Expose MTBF Problems

3 Ways to Expose MTBF Problems

MTBF use and thinking is still rampant. It affects how our peers and colleagues approach solving problems.

There is a full range of problems that come from using MTBF, yet how do you spot the signs of MTBF thinking even when MTBF is not mentioned? Let’s explore there approaches that you can use to ferret out MTBF thinking and move your organization toward making informed decisions concerning reliability. Continue reading 3 Ways to Expose MTBF Problems

Why do we use Weibull++ over JMP?

Why do we use ReliaSoft instead of JMP to Identify the Time to Failure?

This is a question someone posted to Quora and the system prompted me to answer it, which I did.

This question is part of the general question around which software tools do you use for specific situations. First, my response to the question. Continue reading Why do we use Weibull++ over JMP?

Futility of Using MTBF to Design an ALT

Futility of Using MTBF to Design an ALT

Let’s say we want to characterize the reliability performance of a vendor’s device. We’re considering including the device within our system, if and only if, it will survive 5 years reasonably well.

The vendor’s data sheet lists an MTBF value of 200,000 hours. A call to the vendor and search of their site doesn’t reveal any additional reliability information. MTBF is all we have.

We don’t trust it. Which is wise.

Now we want to run an ALT to estimate a time to failure distribution for the device. The intent is to use an acceleration model to accelerate the testing and a time to failure model to adjust to our various expected use conditions.

Given the device, a small interface module with a few buttons, electronics, a display and enclosure, and the data sheet with MTBF, how can we design a meaningful ALT? Continue reading Futility of Using MTBF to Design an ALT

The Damage Done by Drenick’s Theorem

The Damage Done by Drenick’s Theorem

Have you ever wondered by we use the assumption of a constant failure rate? Or considered why we assume our system is ‘in the flat part of the curve [bathtub curve]’?

Where did this silliness first arise?

In part, I lay blame on Mil Hdbk 217 and parts count prediction practices. Yet, there is a theoretical support for the notion that for large, complex systems the overall system time to failure will approach an exponential distribution.

Thanks go to Wally Tubell Jr., a professor of systems engineering and test. He recently sent me his analysis of Drenick’s theorem and it’s connection to the notion of a flat section of a bathtub curve.

Wally did a little research and found the theorem lacking for practical use. I agree and will explain below. Continue reading The Damage Done by Drenick’s Theorem

3 MTBF Stories

3 MTBF Stories

Everyone loves a great story. Storytelling has been a long tradition to pass along knowledge and wisdom.

There are good stories, tales of inspiration. There are sad stories, tales of caution.

There are fables, ghost stores, legends, epic poems, and more. When considering the reliability performance of your product or equipment, you probably have a few stories that you can tell. “That time … “

Simple join colleagues for lunch and ask about the ‘major disasters’ of the past. The stories help us to remember and hopefully avoid repeating mistakes.

Here are three stories with MTBF as a central figure. It is a site and blog that does take about MTBF, so it fits. To start, let me introduce you to Martin, a new reliability engineering reporting to his first day of work at a bicycle design and manufacturing company. Two sad stories and a good one. enjoy. Continue reading 3 MTBF Stories

Different Data Same Decision

Different Data Same Decision

Let say you have some time to failure data on your equipment. A common action is to calculate the MTBF. All well and good until you expect to make a meaningful decision based on the calculation.

Using just the mean of the data, the MTBF value is likely to provide you with a less than useful bit of information. Thus your decision will be rather random or worthless.

Let’s explore just how this simple calculation of perfectly good data can mislead your decision making. Continue reading Different Data Same Decision

How About Weibull Instead of MTBF?

What About Weibull, Can I Use it Instead of MTBF?

This was a follow up question in a recent discussion with Alaa concerning using a metric other than MTBF.

The term ‘Weibull’ in some ways has become a synonym for reliability. Weibull analysis = life data (or reliability) analysis. The Weibull distribution has the capability to describe a changing failure rate, which is lacking when using just MTBF. Yet, it is suitable to use ‘Weibull’ as a metric? Continue reading How About Weibull Instead of MTBF?