AiVibeQuality, Reliability & Maintenance
Mean Time to Failure (MTTF)
Mean time to failure is the expected operating time until failure of a non-repairable item, or until the first failure of a repairable one, estimated from the total operating time of a population divided by the number of failures.
MTTF describes items that are replaced rather than repaired when they fail, such as bearings, lamps, fuses, seals and many electronic components. It is the mean of the time-to-failure distribution. For a population operated until failure, it can be estimated as the total operating time of all units divided by the number of failures. If failures follow an exponential distribution with a constant failure rate, MTTF equals the reciprocal of that rate; for other distributions, such as the Weibull distribution, it is calculated from the distribution parameters.
MTTF is used in component selection, reliability prediction, warranty analysis and spare parts planning. In functional safety, the related quantity MTTFD, the mean time to dangerous failure, is used in ISO 13849-1 to estimate the performance level of safety-related parts of control systems. Rolling bearings are more often specified by rating life, such as L10, the life that 90 per cent of a group of identical bearings is expected to reach or exceed.
MTTF does not indicate when a particular item will fail, and a large share of a population can fail before reaching it, depending on the distribution. Estimates from tests with few failures, or with many units still running, require statistical methods that account for censored data. Field conditions such as load, temperature and contamination can make actual life differ greatly from catalogue values.
Key points
- Applies to non-repairable items or to the first failure of repairable ones.
- With a constant failure rate, MTTF is the reciprocal of the failure rate.
- MTTFD, mean time to dangerous failure, is used in ISO 13849-1.
- Data from units still running are censored and need suitable statistics.
Where AiVibe comes in
AiAmbA AI Factory use cases include predictive maintenance and visual inspection on textile, automotive and electronics lines. AiAmbA IoT normalises industrial signals from protocols such as OPC UA and MQTT, with driver availability confirmed per installation.
Explore AiVibe’s work in Quality, Reliability & Maintenance →