Research & Best Practices

MTTF Vs. MTBF: Explaining Failure Metrics


Maintenance metrics, including failure metrics, are among the most direct and effective ways to track performance and ROI for your maintenance practices and investments. Gauging these metrics can provide valuable insight into the quality of your equipment and the efficacy of your maintenance strategies. MTTF and MTBF are two of the most useful failure metrics available, so it’s helpful to understand what they measure and how to use them.

MTTF vs. MTBF: What do they mean?

MTTF (mean time to failure) and MTBF (mean time between failures) are useful, yet distinct, metrics for equipment failure analysis. They are defined as follows:

MTTF: Mean time to failure is measured only for assets that can’t be repaired. This metric measures and tracks the time span between the installation of a part and the point at which it can no longer fulfill its function in a useful way.

MTBF: Mean time between failures measures assets that can be repaired, and is defined as the time between the installation or fulfillment of a repair and the time when a part is no longer useful and must again be repaired or replaced. In this definition, “failure” does not refer to total component or part failure, but rather to the inability to operate as intended.

Improving MTBF with good maintenance practices

While MTTF is an important measure of nonrepairable part performance, it generally is not a metric that can be directly impacted. Low MTTF may mean that a higher-quality component or vendor choice is required or that those components are being overworked or overloaded. MTBF, on the other hand, can be improved through good maintenance practices and planning, and can also be used to improve current maintenance tactics and efficiency.

Here are a few examples:

MTBF can give you more control over downtime: With enough MTBF data, you can generate more reliable estimates for when a part will need to be taken out of service due to failure. Using this data in conjunction with real-time asset performance data can provide a highly accurate estimate of when repairs will be needed, giving you more predictability and control over downtime, therefore resulting in a more targeted and efficient maintenance plan.

Regular maintenance can extend MTBF: As we mentioned before, good maintenance practices will extend your MTBF and lower overall equipment downtime, especially if you utilize preventive or predictive maintenance practices.

MTBF + predictive maintenance = improved equipment performance: Using MTBF as a data point for predictive maintenance can help you make decisions that will ultimately improve the overall quality and performance of your equipment. This metric can also help you extend the useful life of your assets as it will arm you with the knowledge necessary to address issues before they cause unexpected downtime – or even potential  damage – to components and other equipment.

Tracking these failure metrics is an important component of good maintenance practice as they are key indicators of your overall productivity and efficiency. Backing up your maintenance strategy with data can have a measurable effect on your bottom line – when utilized properly.

Do you want to improve your maintenance efficiency but are feeling overwhelmed by data? As experts in predictive maintenance with over three decades of experience in manufacturing asset management, ATS has the resources and knowledge available to help get you started. To learn more about how our preventive maintenance services can improve your equipment performance metrics, output quality and overall production efficiency, contact us today.

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