# Mean Time Between Failures Calculation

## Meaning of Mean Time Between Failures

Mean Time Between Failures calculation is a way to predict the time between failures of a piece of equipment during normal operating hours. Mean time between failures (MTBF) is the average time between breakdowns of systems. It is a crucial maintenance metric to measure the performance, reliability, and safety of critical assets, like process analyzers. Mean time between failures is used to calculate the availability, together with mean time to repair (MTTR).

## Mean Time Between Failures Calculation

Mean Time Between Failures (MTBF) is a basic measure of asset reliability. MTBF is calculated by dividing the total uptime with the number of breakdowns. Mean Time Between Failures calculation= Total uptime / Number of breakdowns (https://en.wikipedia.org/wiki/Mean_time_between_failures) For example, If an asset had an uptime of 1.000 hours and broke down 10 times, the MTBF calculation would result in 1000/10 = 100 hours. It is crucial to collect data about the performance of the asset. That information is necessary to measure the MTBF. Each asset operates under different circumstances and is influenced by human factors, such as assembly, design, and maintenance. That is why an MTBF estimation should never be made based on a manual.
A high MTBF means fewer problems will occur over an asset’s lifetime. This means lower costs associated with repairs and downtime. A lower MTBF means that asset experiences more frequent failures over its lifetime. When this is the case a strategic asset management tool can help to avoid failures in the future, Like AML Information Management.

## How to use Mean Time Between Failures?

MTBF calculation provides an indication about the period within which an asset can fail and how often an asset will fail. When paired with other maintenance strategies, like root cause analysis, it will help to avoid costly breakdowns. Having this information makes it easier to create preventive maintenance. The reliability can be improved by avoiding issues before they cause failure. Gathering all the data when a failure occurs is essential to improve maintainability.