Mean Time Between Failure Formula And Examples

How frequently does your equipment fail? The mean time between failure (MTBF) formula isn’t complicated—all the complexities are introduced upon implementation. However, you will need to collect the right data and analyze it correctly to yield actionable insights.

Today, we will explore:

• The mean time between failure formula
• How to use the formula effectively (with examples)
• How to reduce your mean time between failure

If you need to know more, reference Mean Time Between Failure (MTBF) and MTTR: a Complete Guide.

So, let’s dive in and look at the mean time between failure and some examples.

What Is Mean Time Between Failure?

Mean Time Between Failure (MTBF) is a metric used to measure the average time between breakdowns or equipment failure. Simply put, it tells you how long an asset typically runs before it breaks down. However, every asset will fail, but being able to predict when, will set you up for success.

Understanding your MTBF provides several benefits. It educates you on predicted failure rates (and, consequently, equipment availability), helps you plan ahead, and tells you whether your maintenance processes are successfully extending your MTBF.

Mean Time Between Failure Formula

MTBF = operational hours / number of failures

MTBF is a simple and key metric for predicting and proactively responding to equipment breakdowns. In the simplest terms, it defines whether your organization is improving its failure rate.  An increase in failure rate could indicate issues with machine or equipment quality. Or it could indicate issues with operations or maintenance practices.

But because MTBF is a simple metric, it also requires the right data inputs—and the right analysis. 

Calculating Mean Time Between Failure

Using the above-listed formula for MTBF, suppose a piece of machinery operates for 1,200 hours over six months and experiences four failures during this period. The MTBF calculation would be:

MTBF = 1,200 hours ÷ 4 failures = 300 hours/failure

Analyzing MTBF

• Higher MTBF: Indicates more reliable equipment with less frequent breakdowns.
• Lower MTBF: Suggests frequent equipment failures, signaling the need for better maintenance or potential equipment upgrades.

Real-World Mean Time Between Failure Examples

In 2018, “Improvement of process machinery availability and reliability: A case study of the production line in a sugar processing plant” was presented at the International Association for Management of Technology conference.

The study of this real-life sugar processing plant identified issues such as insufficient or incorrect downtime data, poorly trained maintenance staff, and delays in logistics and administration. Together, these issues impacted MTBF rates—and, the study concluded, all issues could be avoided through comprehensive and capable computerized maintenance management systems (CMMS) software.

Here are examples of mean time between failure applications.

Asset-Based MTBF

A water treatment plant has three essential pumps. Here, the MTBF calculation is not complex: there are only three assets to track. However, the MTBF calculation is absolutely essential: the water treatment plant must collect the correct data because it cannot risk its assets going down unexpectedly.

With LLumin’s CMMS+ software, the plant would collect both historical data regarding failures and sensor-based and machine-level data based on each pump’s performance. As a result, more accurate MTBF rates would avoid catastrophic failure.

Manufacturing Facility-Based MTBF 

A manufacturing facility has hundreds of machines for multiple stages of the manufacturing process, and each stage of the process involves numerous models. MTBF rates must be tracked discretely across each make and model of machine. Not only is the facility’s availability based on machines but also its manufacturing stages. If a single stage or work cell is unavailable, then production grinds to a halt.

LLumin’s CMMS+ software streamlines and optimizes MTBF tracking through maintenance work order processes, machine learning technology, historical data, and sensors.

Fleet-Based MTBF

In a fleet, MTBF calculations refer to like-kind assets—such as a “fleet” of aircraft. These assets come from consolidated pools, but MTBF is still essential in understanding the availability of those pools. A car rental service, for instance, might be operating with very low availability at all times—not understanding their MTBF could lead to bottlenecks and unhappy customers.

Companies can schedule maintenance proactively by tracking fleets through a CMMS. And if the company changes its maintenance processes or best practices, it can use the CMMS’s MTBF data to determine whether these processes have truly improved the organization’s efficiency.

How To Improve Mean Time Between Failure

The mean time between failure formula helps maintenance managers and reliability engineers anticipate and reduce failure rates. While it’s important to know when an asset will break down (and every asset will eventually break down), it is likewise important to reduce downtime through scheduled and predictive maintenance.

Traditionally, improving MTBF occurred in a few different ways:

• By tracking assets, maintenance, and effective repairs.

Example: You can increase uptime and operational efficiency by routinely tracking and changing machine filters (and removing vulnerabilities).

• Standardizing work processes and work orders. 

Example: Creating a comprehensive log of when every vehicle in a fleet is serviced.

• Inventory control, management, and buffers. 

Example: Always having the parts needed (or a replacement) for critical pumps in a water treatment facility.

• Appropriately timing maintenance inspections and tasks. 

Example: Following best practices provided by the manufacturer for an industrial machine.

LLumin’s CMMS+ software augments all these practices with next-generation, machine and operations data-driven tracking. With LLumin’s tracking (and machine-learning algorithms), organizations can more effectively track their assets, standardize work processes, control inventory availability, and time inspections and repairs.

Next-Generation  Maintenance Using Control Systems Technology

LLumin’s next-generation maintenance technology leverages control system data to both predict and lengthen MTBF rates. 

By collecting and analyzing data across the entirety of the organization, LLumin can dramatically improve MTBF rates—without having to rely on technicians or administrators for that data.

And then, through real-time, machine-level insights into system performance, LLumin can also reduce failure rates and increase your MTBF levels. Imagine a wind farm with hundreds of wind turbines. LLumin’s CMMS+ software can identify the signs of a potential failure, such as heat increases or efficiency decreases, before a scheduled maintenance inspection. Thereby creating actions that occur at the right time.

LLumin will automate your MTBF KPI strategy, including the use of machine-level analytics that helps you anticipate failure before it occurs. With LLumin, you can achieve complete control over your maintenance, repair, and production schedules to keep your operations moving optimally.

FAQs

How Do You Calculate MTBF?

To calculate MTBF, use the following formula: MTBF = operational hours / number of failures. Before calculating your MTBF, you need to ensure that you have the correct data. A CMMS will automatically track operational uptime and failure rates.

What Is the Formula of MTTR and MTBF?

Use MTTR and MTBF together to calculate system-wide availability. MTBF is calculated as operational hours / number of failures. MTTR is calculated as total maintenance time / number of repairs.

How Do You Calculate the Reliability of MTBF?

MTBF is calculated by dividing the operational hours of an asset by the number of breakdowns that occurred. However, it is not a good measurement for “reliability” because it is a fairly light metric. A CMMS will take both historical data and predictive, sensor-based data to calculate the true reliability of an asset.