Reliability calculations give you insights into equipment faults and show you exactly where to focus your maintenance efforts. This translates to less unplanned downtime, greater productivity, and lower maintenance costs. Learning reliability calculations can also empower your maintenance team to build and execute effective, proactive maintenance strategies.

To achieve this goal, modern plants use a number of different maintenance metrics to gauge their assets’ reliability and availability. Two of the most commonly used metrics are Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR). Other metrics, like the system reliability formula, are also useful maintenance KPIs with insights into asset performance.

What do these metrics have in common? They rely on large-scale data collection. While collecting this volume of data was once manual and time-consuming, it can now be done rapidly at scale using affordable Industrial Internet of Things, or IIoT, devices that constantly measure your machine’s health and performance metrics. Modern, cloud-based computerized maintenance management system (CMMS) software can help manage your organization’s data with dashboards and easy-to-use reports.

How Do You Measure Reliability and Maintenance Metrics?

All reliability calculations rely on data collection. To calculate reliability accurately, your maintenance team will need to gather data on all your key assets and equipment.

You’ll need to track how often your assets break down, how often they require maintenance, and how many hours they are in operation. It’s also a good idea to track condition monitoring data like vibration measurements, temperature, power quality, and oil analysis. The more detailed and granular you can get, especially for your most critical assets, the better the picture of each asset’s reliability you’ll get.

Calculate Reliability Using MTBF

Mean Time Between Failure (MTBF) is a measure of the average length of time between asset breakdowns. It is calculated by dividing an asset’s uptime by the number of failures experienced in that period of time.

If your MTBF is low, it means that your asset is breaking down often and needs frequent maintenance. If your MTBF is high, it means that your equipment can stay up and running for long periods before failing. MBTF is normally expressed in hours and tells you, on average, how long the machine will be able to operate before it’s expected to break down again.

When is MTBF Useful?

Calculating MTBF can help determine whether to repair or replace a particular asset. If a piece of equipment has a very low MTBF, it may be worth investing some time and effort to try and improve this metric. If you consistently fail to improve the MTBF and the machine keeps breaking down shortly after maintenance, then it may be time to replace it.

Knowing this frees up your maintenance team to put their time into an asset that responds better to repairs. MTBF can also help drive inventory decisions so that you always have spare parts on hand. If you know roughly when a component part is likely to break down, then you’ll know when to stock spare parts.

How Do You Calculate Failure Rate?

An asset’s failure rate changes over the course of its lifecycle. When the asset is new, its failure rate is at its lowest point. But as the asset reaches the end of its useful life, its failure rate increases dramatically. You can calculate an asset’s failure rate using the same data as you’d use to calculate MTBF. Simply divide the number of failures by the number of operating hours.

How Do You Calculate System Reliability?

System reliability measures the reliability of a major asset, one which consists of a number of component parts. System reliability refers to the percentage of time that the major asset is available, without any failure or breakdown.

The system reliability formula builds on the failure rate metric. Once you know the failure rate of each component part of an asset, you can use that to calculate the overall reliability of the entire system.

The formula looks like this:

R = (1-F1) * (1-F2) * (1-F3) * (1-F4) …

R refers to the overall reliability of the system, or asset.

F1 refers to the failure rate of the first component part; F2 is the failure rate of the second component part, and so on.

System reliability is a useful metric for anyone who wants to get an overall view of their system performance.

Using Reliability Calculations with Predictive Maintenance

Predictive maintenance depends on IIoT technology to collect data directly from your assets. In many cases, condition monitoring tools can collect and analyze data on a near-continuous basis. That means a constant stream of insights into your asset condition and maintenance needs.

Modern tools make it easy to track and record data. For example, wireless vibration monitors keep track of changes in vibration and temperature. Meanwhile, a CMMS stores and analyzes data on breakdowns and work orders, so that you can easily identify patterns in each of your assets.

That level of data collection makes a big difference when it comes to ensuring accurate reliability calculations and improving your reliability metrics. Predictive maintenance allows your maintenance teams to plan ahead and fix issues early so that you can avoid unplanned breakdowns or drops in productivity.

The result is lower maintenance costs, increased uptime, and greater efficiency throughout your plant.