Mean Time Between Failures

Key Takeaways

• Mean time between failures (MTBF) is a calculation that quantifies how long a system runs on average before a disruptive malfunction. 
• MTBF testing is an important part of maintenance planning across multiple industries, including solar, wind, and telecommunications
• Each industry typically has its own set of MTBF standards to provide guidance on testing and measurement

What is Mean Time Between Failures (MTBF)?

Mean time between failures (MTBF) calculates the average amount of time a repairable system operates between unplanned failures. It is a core reliability metric used to assess availability, maintenance effectiveness, and operational risk. Alongside mean time to repair (MTTR), it’s a key indicator of system reliability and availability. It also helps technicians minimize downtime through proactive maintenance while providing a reliability benchmark for multi-component systems.

MTBF is most frequently used in telecommunications, industrial power, and renewable energy, including solar and wind, among others. In more complex systems with non-repairable components, it’s often paired with mean time to failure (MTTF).

How to Calculate MTBF

Measured in hours, MTBF is calculated by dividing the total operational time for a specific period by the number of failures within that same period. For instance, if a wind turbine experiences three failures within 3000 hours, its MTBF would be 1000 hours. This means that, on average, the turbine remains operational for roughly a thousand hours at a time before malfunctioning. 

Understanding and Contextualizing MTBF

Although valuable, MTBF isn’t without its limitations. It doesn’t generally account for environmental or operational conditions, instead assuming a constant failure rate. For this reason, it’s important to assess MTBF alongside additional metrics such as:

• Temperature
• Humidity
• Vibration
• Load conditions
• Air quality
• Wind speed
• Air pressure

Other telecom and MNO-specific metrics that influence MTBF include:

• Wind load exposure
• Corrosion index
• Thermal load profile
• Ice and snow loading
• Lightning strike density
• Tower vibration levels
• RF load and transmission duty cycle
• Backhaul utilization and switching load
• Power quality and generator runtime
• Battery discharge cycles
• Ingress and environmental sealing score
• Antenna and RRU elevation stress
• HVAC performance metrics
• Vendor-specific component reliability
• Site accessibility and maintenance frequency

Historical field data can also provide additional context to MTBF calculations by showing how reliably a system performed in the past. Operators should also consider equipment utilization, component quality between different suppliers, and a system’s failure modes. Lastly, stress test data can provide insight into how harsh conditions or intense usage might impact a system’s MTBF. 

Common MTBF Standards

Because MTBF originated in electronics and defence engineering, standards tend to skew heavily toward systems with electronic components. For this reason, they tend to be most relevant to telecommunications operators. The construction industry typically focuses more on safety and reliability, while wind and solar lack formal standards due to wildly variable environmental conditions.

Some of the most commonly accepted standards are listed below. 

Note that although no formal MTBF standards exist for industries such as solar energy, organizations can still test and measure their MTBF by applying standards such as Siemens SN 29500. Understand, however, that this is more of a practical workaround than a recognized or industry-mandated framework. 

Digital twins turn MTBF from a theoretical average into a site-specific, evidence-based metric.

Instead of assuming constant failure rates, operators can track how real-world changes such as load shifts, mounting deviations, corrosion, weather exposure, or component aging, affect reliability over time. 

• For towers, a detailed structural and equipment model helps teams track load changes, mounting shifts, corrosion, and weather exposure that influence real-world failure rates.
• In solar, repeatable thermal and visual models reveal degradation patterns, hotspot development, and component aging that shape reliability expectations over time. 
• In wind, blade-level models make it easier to monitor erosion, cracking, and lightning damage, giving operators clearer insight into how these faults affect reliability over the turbine’s lifecycle.

By comparing each new capture against the previous state, operators can see how assets are trending and refine MTBF forecasts with greater accuracy..

FAQs

What does it mean when a system has a high MTBF? 

Generally, a higher MTBF is a good thing, as it indicates that a system is operational for much longer before experiencing unplanned downtime. 

Can MTBF be applied to non-repairable systems? 

No. MTBF is specifically for systems that are either fully repairable or have replaceable components. The metric for systems that aren’t repairable is Mean Time To Failure (MTTF). More complex systems may leverage both MTTF and MTBF. 

What’s the relationship between MTBF and system availability?

MTBF positively correlates with system availability. A higher MTBF means a system is available far more frequently, while a lower MTBF typically indicates potential availability concerns.

What’s the difference between MTBF and system lifespan? 

System lifespan measures how long a system or component is expected to last before it can no longer be repaired. MTBF represents the average time between failures, and is typically measured on a far shorter timescale. 

How can I improve MTBF? 

Potential measures that can be taken to improve MTBF include:

• Preventative maintenance processes
• Leveraging higher-quality components
• More frequent inspections via autonomous drone technology
• Designing systems for redundancy and reliability