What is Mean Between Failures (MTBF)?
Mean Time Between Failure (MTBF) indicates the average length of time between equipment breakdowns. Usually expressed in hours, MTBF is used for large, complex assets that are capable of being restored to working order. The calculation specifically accounts for the time between unplanned shutdowns — not scheduled maintenance.
MTBF helps teams better anticipate and prepare for potential issues in advance and ultimately empowers maintenance teams to reduce costly unplanned downtime. MTBF is an important maintenance KPI for tracking the overall effectiveness of your maintenance efforts. For example, a shorter MTBF means that assets are breaking down frequently and improvements are needed. Meanwhile, a longer MTBF indicates that operations are running smoothly, and asset availability is high.
Used in conjunction with MTTR (mean time to repair), and MTTF (mean time to failure), MTBF provides a clearer understanding of asset availability and the effectiveness of your maintenance programs. MTTF, MTTR and MTBF are metrics that can be tracked over time to measure the impact of improvements to your asset maintenance programs.
How to Calculate MTBF
To calculate an asset’s MTBF, you’ll need to know the asset’s total hours of operation and the number of unplanned shutdowns the asset experienced during that time.
Then, you can use this formula to determine an MTBF score:
Hours of Operation / Number of Failures = MTBF
How to Use MTBF
MTBF can help answer questions like:
When should an asset be repaired?
Knowing the average amount of time between failures helps teams estimate the likelihood an asset will fail within a given timeframe. Maintenance teams can use MTBF to identify patterns between failures, pinpoint common issues, and focus their efforts where it matters most.
Should an asset be repaired or replaced?
The average time between failures helps teams determine whether to replace or repair an asset, enabling data-driven capital expense (CapEx) decisions. For example, if an asset’s MTBF is consistently low and all efforts to improve this metric fail, it is probably time to replace the asset entirely rather than continue to repair it.
When should new parts be ordered?
MTBF makes it possible to forecast replacement part requirements and plan ahead for just-in-time delivery, ensuring spare parts are on hand when a breakdown occurs. This saves costs and shortens the time to repairs.
Use MTTR and MTBF Together to Determine Availability
MTBF measures an asset’s reliability, or how likely an asset is to fail. It is one-half of an equation to determine a machine’s availability, alongside Mean Time to Repair (MTTR). MTTR measures how quickly a maintenance team can restore an asset to working order and re-integrate it into the production line. Armed with MTTR and MTBF together, a team can have a better idea of an asset’s ability to perform at capacity when it’s needed most, eliminating most, if not all, unexpected periods of downtime.
Following is the formula to determine an asset’s practical availability with MTBF and MTTR:
MTBF / (MTBF + MTTR) = Availability
For example, if a machine can run for 60 hours before it needs to be repaired (MTBF), we can add that to the 10 hours it takes to repair it (MTTR) to get 70. This number is then divided that by the original MTBF …
60 / 70 = .86
… or an 86% availability. MTBF provides a tangible measure of an asset’s availability and reliability so action can be taken to improve its uptime, performance, and lifespan.
How to Improve MTBF with Condition Monitoring and Predictive Maintenance
Improving MTBF and reducing unplanned downtime has become easier with the emergence of IIoT (Industrial Internet of Things) technology. Where in the past, maintenance teams relied on scheduled maintenance to keep their machines in good repair, now they can leverage real-time data and insights from the machines themselves to predict maintenance requirements and prevent unexpected downtime. This is known as predictive maintenance.
Predictive maintenance relies on sensors that monitor key conditions like vibration and temperate that indicate machine health. This is called condition monitoring. For example, connected vibration sensors detect increases in vibration, which is an early indicator of an upcoming failure for rotating equipment. This data enables teams to proactively repair small problems quickly — before they lead to breakdown.
This data can be integrated into your Computerized Maintenance Management System (CMMS) software.
Predictive maintenance and condition monitoring are key to improving MTBF and adopting a more proactive maintenance program that avoids unexpected shutdowns. As a result, many organizations are adopting condition monitoring as a key component in their maintenance strategy.
Speak with a specialist to learn how a CMMS can help track maintenance KPIs and condition monitoring sensors can help avoid downtime altogether.