Mean Time to Failure (MTTF) is a maintenance metric that measures the average time a non-repairable asset operates before it fails. The only equipment that you should include in your MTTF metric are assets that:
- Cannot be repaired
- Should not be repaired (due to high cost or other reasons)
MTTF is also described as the average lifespan of a non-repairable asset. You can use this metric to calculate maintenance intervals and support decision-making around equipment replacement.
The decision to replace equipment is purely financial with the expected MTTF, repair costs, and downtime for each piece of machinery. Of course, you must also factor in other things such as company policy or legal obligations due to safety or government regulation.
You can gather MTTF data through reliability testing and field observations. Under controlled conditions, a small sample of units is tested for failure. Field observations involve monitoring installed units over time and recording failures. Additionally, you can estimate mean time to failure using statistical methods such as survival analysis. MTTF estimates are important for planning purposes but should be updated as more data becomes available.
How to Calculate Mean Time to Failure
Divide the total number of hours of operation by the total number of assets in use to calculate MTTF. For example, say you have three fan belts that lasted 3,000 hours, 4,000 hours, and 3,700 hours. Here’s what the calculation would look like:
MTTF = (3,000 + 4,000 + 3,700) / 3
MTTF = 10,700 / 3
The Mean Time to Failure of these fan belts = 3,566 hours
Because MTTF represents the average time to failure, calculating it with a higher number of assets will yield more accurate results.
How to Use MTTF to Manage Your Assets
MTTF is a statistic to predict the reliability of non-repairable assets. When an asset fails, you need to replace it. This involves downtime, which can cost you production time if you weren’t planning on this repair. Alternatively, if you know the MTTF of your assets, you can better plan for run-to-failure repairs and minimize unplanned downtime.
Here are some examples of assets or components that you may want to run to failure:
- Fan belts in motors and engines
- Idler balls/rollers on conveyor belts
- Wheels on a forklift
Generally, the key is that it is cheaper to replace the component or asset than it is to repair it. You wouldn’t take a lightbulb apart and attempt to repair it, as it would be time-consuming and more expensive than just buying a new lightbulb.
When to Use Mean Time to Failure
You can use MTTF to compare the reliability of different systems or components. Also, it can help identify potential problem areas. In some cases, calculating the mean time to failure will help you to optimize your maintenance and asset management strategy.
- In some cases where regular preventive maintenance can extend the life of a part and a massive, mission-critical asset, you can use MTTF to schedule maintenance on non-repairable equipment. For example, lubricating bearings on essential equipment.
- MTTF can also be used to make purchasing decisions for parts and equipment. Higher-quality and more durable materials will result in a longer MTTF, which means spending fewer resources on purchasing new and replacing old parts.
- MTTF is beneficial in developing a just-in-time inventory strategy. If a facility knows a specific part has an MTTF of 10,000 hours and a replacement takes 100 hours, they can order a part every 9,900 hours.
Understanding mean time to failure can help organizations reduce downtime and develop better maintenance strategies, driven by reduced dependence on reactive maintenance and increased predictive or planned maintenance. The organization can identify which systems are most likely to fail and take steps to prevent or mitigate those failures. MTTF data is beneficial in developing more accurate forecasting models for future maintenance needs. It is a valuable metric for understanding system reliability and reducing downtime. Click here if you’d like to learn more about other common maintenance metrics.