What is mean time between failure (MTBF)?
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What is MTBF?

Mean time between failure (MTBF) is a measure of the reliability of a system or component. It’s a crucial element of maintenance management, representing the average time that a system or component will operate before it fails. 

The MTBF formula is often used in the context of industrial or electronic system maintainability, where failure of a component can lead to significant downtime or even safety risks, but MTBF is used across many types of repairable systems and diverse industries.

It can help measure the overall reliability of manufacturing plants, energy grids, information networks and countless other use cases.

MTBF is calculated by dividing the total time of operation by the number of failures that occur during that time. The result is an average value that can be used to estimate the expected service life of the system or component.

It's important to note that MTBF is an average time, and does not guarantee that a particular system or component will last for the full MTBF period without failing.

The actual time between failures can vary widely, and it is not uncommon for failures to occur well before or after the MTBF. Also, MTBF does not take into account the severity of the failures or the impact they can have on operations or safety.

The MTBF value is a measure of reliability, but it is not a guarantee of reliability. It measures how frequently failures are expected to occur, but doesn’t necessarily take into account every external factor.

Environmental conditions, maintenance practices and usage patterns can impact the reliability of a system or component, so it’s critical to use MTBF as one tool of many to get a more detailed narrative of a system or component’s overall health. Determining the MTBF gives us a useful metric of failure count over time, but doesn’t explain why problems are occurring.

A high MTBF doesn’t mean that breakdowns will never occur, only that they are less likely to occur. All systems and components have a finite lifecycle, and failures can occur due to various factors, including wear and tear, environmental conditions and manufacturing defects.

Reliability engineers can use MTBF to compare the reliability of similar systems or components, but it cannot be directly compared between different systems or components. This is because the MTBF is highly dependent on the operating conditions, usage patterns and other factors specific to the system or component being measured.

It is difficult and possibly inadvisable to seek a meaningful definition of a good MTBF across different use cases. A good MTBF for one system might look different than a good MTBF in another similar use case.

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How is mean time between failure calculated?

First, let’s define the scope. We must define the system or component in question, along with operating conditions, including environmental factors and usage patterns. Then, we collect data on the operating time of the system or component, including the start and end times of each operation cycle.

Then, we record the number of failures that occurred during the operating time. Finally, we can calculate the MTBF by dividing the total operating time by the number of failures. The result is expressed in hours but can be any unit of time.

For example, let's say you want to calculate the MTBF of a motor that operates for 8 hours per day, 5 days a week, for a total of 1 year. During this time, the motor fails 4 times. To calculate the MTBF:

Total operating time
= 8 hours/day x 5 days/week x 52 weeks = 2,080 hours

Number of failures
= 4

= Total operating time / number of failures = 2,080 hours / 4 = 520 hours

The MTBF of the motor is 520 hours. This means that on average, the motor can be expected to operate for 520 hours before it fails. In reality, it might fail sooner, or later than 520 hours, and we won’t understand why the motor is failing, but this average time is a useful metric.

This is a starting point that enables us to get a basic sense of how a system or component is performing in terms of reliability and helps us to analyze trends, which helps us to understand the overall efficacy of our maintenance strategy.

Related terms and tools

Maintenance managers use an array of formula to understand the status of their operations. They increasingly use computerized maintenance management systems (CMMS) within an enterprise asset management (EAM) framework to more readily and frequently derive such information.

Failure rate

The inverse of MTBF is the failure rate, a measurement of the number of failures over time. Instead of expressing this information as an average number of hours, it is expressed as a rate. A failure rate does not correlate with uptime or availability for operation, it only reflects the rate of failure.

Mean time to repair

Another maintenance metric is mean time to repair (MTTR), which represents the average time it would take to restore the uptime of a given component or system. MTTR is used to optimize repair times.

Learn more
Mean time to failure

Maintenance engineers also often have mean time to failure (MTTF) on their checklists. This refers to non-repairable components and systems. These will inevitably fail and will require a total replacement rather than a repair.

Root cause analysis

Another tool is root cause analysis, a methodology for discovering the root causes of problems to identify the best solutions.

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Common challenges for calculating mean time between failure

Calculating MTBF can be challenging due to several factors, including:

Data availability
: One of the biggest challenges in calculating MTBF is the availability and quality of data. To calculate MTBF, data on the number of failures and the operating time of the system or component is needed. If this data is not available or is of poor quality, it can be challenging to accurately calculate MTBF.

Complex systems
: In complex systems with many components, it can be challenging to identify the specific component that caused a failure. This can make it difficult to accurately calculate the MTBF for individual components.

Time frame
: The time frame over which failures and operating time are measured can have a significant impact on the calculated MTBF. If the time frame is too short, the MTBF might not be representative of the true reliability of the system or component.

Maintenance schedules
: Maintenance practices can impact the calculated MTBF. If maintenance teams perform preventive maintenance too frequently, failures might not occur often enough to accurately calculate MTBF. If maintenance is not performed frequently enough, failures might occur more frequently, leading to an artificially low MTBF.

Changing operating conditions
: Operating conditions such as temperature, humidity and vibration can impact the reliability of a system or component. If these conditions change over time, it can be challenging to accurately calculate MTBF.

By addressing these challenges and collecting accurate data, businesses can improve their understanding of system and component reliability and take steps to raise MTBF, reduce the number of failures and resulting downtime and operate more efficiently.

Benefits of mean time between failure

Improving MTBF reduces the number of failures over a given period, providing a range of benefits to businesses and industries. Key benefits include:

Increased reliability
: Improving MTBF can lead to increased reliability of systems and components. This can help businesses reduce downtime, improve productivity and minimize the risk of safety incidents.

Improved customer satisfaction
: By extending operation time and reducing the number of breakdowns and resulting outages, businesses can produce higher-quality outputs at lower costs, enabling them to improve customer satisfaction. This can also lead to increased customer loyalty and repeat business.

Lower maintenance costs
: By identifying potential issues before they result in unplanned downtime, businesses can develop smarter maintenance strategies, and reduce overall maintenance costs. Preventive maintenance is often less costly than reactive maintenance.

Longer lifespan of equipment
: Improving MTBF can lead to longer lifespans for pieces of equipment. This can help businesses reduce capital expenditures and extend the useful life of assets.

Better quality control
: Improving MTBF often involves improving quality control during manufacturing. This can lead to fewer defects and improved product quality.

Enhanced safety
: In industries such as aerospace, defense and healthcare, improving MTBF can enhance safety by reducing the risk of component or system breakdowns.

Improving MTBF can provide a range of benefits to businesses and industries.

How to improve mean time between failure

Improving MTBF often involves identifying and addressing the root causes of failures. Here are some common ways to improve MTBF:

Design improvements
: Design changes can improve the reliability of a system or piece of equipment by addressing potential failure points. This might include using higher-quality materials, adding redundancy or improving the design of critical components.

Preventive maintenance
: Regular maintenance and inspection can identify potential issues before they lead to breakdowns. Preventive maintenance can include tasks such as lubrication, cleaning and replacing worn or damaged parts.

Training and education
: Proper training and education can help reliability engineers identify potential issues and perform maintenance tasks correctly. This can include training on proper operation procedures, troubleshooting techniques and maintenance tasks.

Improved testing and quality control
: Improved testing and quality control during manufacturing can help identify and address potential defects before they reach the customer. This can include testing for defects during the manufacturing process, and quality control checks before shipping.

Data analysis and monitoring
: Data analysis and monitoring can help identify trends and patterns that can lead to failures. By analyzing data from sensors, logs and other sources, potential issues can be identified and addressed before they cause a failure.

Overall, improving MTBF requires a systematic approach to identifying and addressing potential causes for downtime at every stage of a system or component's lifecycle. By improving design, maintenance, training, quality control and monitoring, MTBF can be increased, leading to increased reliability and uptime.

Common use cases for mean time between failure

There are many arenas where MTBF can be a useful tool to calculate the number of failures across a given period of time.

Electronics and semiconductors

In the electronics and semiconductor industry, MTBF is a useful metric to determine the reliability of repairable items and systems such as microchips, circuit boards and power supplies.

MTBF is often used in the design and testing phase to help ensure that components meet reliability requirements.


MTBF is used in manufacturing to measure the reliability of pieces of equipment. By performing the MTBF calculation on machines, manufacturers can identify potential issues and schedule maintenance or replacement before a failure occurs, which can lead to costly downtime and lost productivity.



Aerospace and defense

MTBF is critical in the aerospace and defense industry, where the breakdown of a component can have serious safety implications. When human lives are on the line, it is essential to maximize the total uptime of critical systems like fuel and oxygen supply systems.

MTBF is used to help ensure that components and systems meet reliability requirements and to identify potential issues before they become safety risks.



MTBF is used in the automotive industry to measure the reliability of components such as engines, transmissions and electronic systems.

By tracking MTBF, manufacturers can identify design or manufacturing issues and take corrective action before a failure occurs.

Medical devices

In the medical device industry, MTBF is used to help ensure that devices such as pacemakers, insulin pumps and MRI machines meet reliability requirements and do not pose a risk to patient safety.

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