¿Qué es el tiempo medio hasta el fallo (MTTF)?
Mean Time to Failure (MTTF) is a maintenance key performance indicator (KPI) used to track the lifespan of assets that cannot be repaired and therefore must be replaced. Knowing the lifespan of these assets allows your business to make strategic decisions regarding inventory, production, and maintenance to ensure maximum equipment uptime.
El MTTF mide el tiempo transcurrido entre la instalación de un activo y el punto de fallo. Indica el número medio de horas que durará cada elemento, pieza o componente antes de que sea necesario sustituirlo.
El MTTF se utiliza específicamente para activos que no pueden repararse. Normalmente, sustituir estos activos es menos caro y más eficiente que repararlos.
Como tal, el tiempo medio hasta el fallo es un cálculo útil para activos más pequeños y componentes individuales dentro de maquinaria más grande, como por ejemplo:
- Bombillas
- Wheels
- Conveyor rollers
- Ball bearings
- Fan belts
- Transistors
Knowing a part’s MTTF gives maintenance teams a clearer understanding of the reliability of the parts that make up a larger production asset. It also gives them actionable insights, so they can stay ahead of their maintenance needs.
Aunque algunos componentes individuales no pueden repararse, a menudo forman parte de un activo mayor que puede repararse. La longevidad y fiabilidad de estos componentes puede tener un gran impacto en el tiempo de actividad de los equipos y en el rendimiento general de activos más grandes.
Once maintenance teams know each component’s MTTF, they can use that number to manage their inventory and make smarter decisions about when to purchase parts. For example, in just-in-time manufacturing environments, MTTF metrics can be used to predict when to order parts so they arrive as they are needed.
¿Cómo se calcula el MTTF?
Calcular el MTTF requiere conocer el historial del activo. Tendrá que saber cuántas piezas concretas se han utilizado y cuánto ha durado cada una de ellas. Más concretamente, tendrá que calcular el número de horas que se utilizó cada pieza entre la instalación inicial y el fallo/sustitución. Cuantos más datos tenga, más preciso será el cálculo del MTTF.
That’s why MTTF is best used for components that fail relatively quickly. Otherwise, it would take years to determine the MTTF score for more durable equipment. It’s also important to keep in mind that MTTF should only be calculated between identical assets.
Por ejemplo, si está calculando el MTTF de las bombillas, asegúrese de utilizar sólo bombillas con la misma potencia. Las diferencias en estas variables conducen a resultados inexactos e inutilizables.
A continuación te explicamos cómo calcular el MTTF con su fórmula exclusiva.
Fórmula del tiempo medio hasta el fallo (fórmula MTTF)
An asset’s MTTF is calculated by dividing the total operating hours by the number of specific assets in use.
Puedes calcularlo utilizando la fórmula del MTTF:
Total de horas de funcionamiento / Número de activos = MTTF
Dado que las horas de funcionamiento se dividen entre varios activos, tendrá que sumar sus horas totales. Por ejemplo, si intentara calcular el MTTF de un tipo específico de bombilla, necesitaría la vida útil de cada bombilla.
Ejemplo de cálculo del tiempo medio hasta el fallo
Por ejemplo, supongamos que tiene seis bombillas: tres con una vida útil de 1.000 horas y tres con una vida útil de 1.200 horas. En total, las bombillas funcionan durante 6.600 horas. Su cálculo del MTTF sería el siguiente
6.600 horas totales de funcionamiento/ 6 bombillas = 1.100 horas
Así, cada bombilla tendría un MTTF de 1.100 horas.
¿El MTTF debe ser alto o bajo?
Since MTTF is the mean number of hours until the point of failure, a high MTTF is better. This means your asset will last longer before failing.
A low MTTF indicates that your asset is likely to fail sooner, relative to an asset with a high MTTF. So, when you seek to “improve MTTF,” you’d aim for a higher number. However, it’s important to remember that each asset will have its own MTTF. For example, a high MTTF for a bearing may be 4,000 hours, while an MTTF of 4,000 hours for a conveyor belt may be unacceptably low. It all depends on the assets and their applications.
What Is a Good MTTF?
After you’ve entered your figures into the MTTF calculation, how do you determine if it’s a good MTTF for the asset? While there isn’t a universal answer to this question, maintenance teams may consider several factors to find out if they are achieving a good MTTF.
- Expected lifecycle: Compare your MTTF to the expected lifecycle of the component, either the one provided by the manufacturer or based on your own historical data. If your MTTF is longer, that means your components are lasting longer than expected and you’re getting a “good” MTTF. However, if your MTTF is shorter than expected, it may indicate you need to adjust your maintenance practices or purchase parts from a new supplier.
- Operational environment: The asset’s use environment can impact MTTF. A good MTTF for an asset operating in a harsh environment could be shorter than industry standards, but it could still be good when considered in its entire context. For example, ball bearings operated in a very humid environment may have a shorter MTTF than those operated in a less humid environment, but both MTTF numbers could still be considered “good”.
- Component variability: Not all components are created equal, even within the same category. Differences in material quality, design, or manufacturing processes can all impact MTTF. When comparing results, make sure you’re looking at components of a similar make, model, and manufacturer to get an accurate picture of their MTTF.
- Maintenance and usage patterns: MTTF also varies based on how consistently preventive maintenance is performed and how the asset is used. Excessive workloads or runtime, improper installation, or skipped maintenance tasks can shorten MTTF.
A good MTTF depends on many different variables, but tracking this metric and observing how it changes over time is an important part of successful maintenance management.
¿Por qué es importante la MTTF?
El MTTF es una métrica crítica en ingeniería de fiabilidad y gestión del mantenimiento, que ofrece varios Beneficios clave. En primer lugar, proporciona una indicación clara de la vida útil prevista de los componentes no reparables, lo que permite a las organizaciones planificar las sustituciones y gestionar eficazmente el inventario. Al conocer la duración media prevista de un componente, las empresas pueden programar las sustituciones oportunas, minimizando el tiempo de inactividad y manteniendo la eficiencia operativa.
Secondly, MTTF is essential for quality assurance and product development. By analyzing the MTTF of various components, engineers can identify areas for improvement and enhance the overall reliability of products. This, in turn, leads to increased customer satisfaction and fewer warranty claims. MTTF also plays a crucial role in cost management. By predicting the lifespan of components, companies can budget for replacements and maintenance activities more accurately, avoiding unexpected expenses.
Además, el MTTF es vital para las aplicaciones críticas para la seguridad. En sectores como el aeroespacial, la sanidad y la automoción, conocer el MTTF garantiza que los componentes se sustituyan antes de que fallen, evitando fallos catastróficos y garantizando la seguridad de los usuarios. En general, el MTTF es una métrica indispensable que ayuda a las organizaciones a mejorar la fiabilidad, gestionar los costes y garantizar la seguridad operativa.
El MTTF ayuda a responder a importantes preguntas de mantenimiento
Saber calcular el MTTF puede ayudar a su equipo a tomar decisiones operativas estratégicas. Aunque se utiliza para elementos que no se pueden reparar, conocer el MTTF de los distintos elementos que utiliza su empresa puede ayudar a maximizar el tiempo de actividad y ahorrar dinero.
También podemos definir el MTTF como el tiempo medio hasta el fallo. Aunque esto no es una garantía de cuánto durará cada pieza, sigue siendo útil conocer la media.
He aquí algunas preguntas importantes sobre el mantenimiento que MTTF puede ayudarle a responder:
¿Puede el mantenimiento preventivo alargar la vida útil de los activos?
El MTTF se utiliza mejor para los activos que no pueden repararse. Sin embargo, la vida útil de ciertos activos puede prolongarse realizando un mantenimiento preventivo. Por ejemplo, lubricar los rodamientos de bolas puede ayudar a prolongar su vida útil. También puede ampliar la vida útil de otros activos utilizándolos según las recomendaciones del fabricante.
Understanding MTTF can help you measure just how much preventive maintenance can extend the life of a particular part. With this information, maintenance teams can determine whether the value of conducting preventive maintenance on that part is worth the effort or if a run-to-failure approach is more time and cost-effective.
¿Qué es la calidad de las piezas de recambio disponibles?
La calidad de las piezas de repuesto afecta a la vida útil general de un activo, por lo que elegir piezas de alta calidad es clave para prolongar la vida útil de su equipo y garantizar la eficiencia general. El MTTF puede indicar la calidad de las piezas de repuesto, por lo que podrá tomar mejores decisiones de compra a la hora de adquirir futuras piezas de repuesto.
Los fabricantes de piezas pueden tomar decisiones que afecten a la longevidad y calidad de las piezas que siempre ha utilizado. Cuando conozca la puntuación MTTF de cada artículo y realice un seguimiento a lo largo del tiempo, podrá ver si la calidad general disminuye. Si es así, podría ser un indicio de que ha llegado el momento de comprar piezas a un proveedor diferente.
¿Cuándo hay que pedir piezas de recambio?
In addition to helping you decide what parts to purchase, MTTF can be used to decide when to purchase replacement parts. Keeping an inventory of spare parts on hand is not always feasible due to space and cost constraints. Plus, some materials will deteriorate over time and cannot be stored long-term.
Maintenance teams can instead use MTTF to help them optimize parts ordering so that replacements arrive right before they are needed. A robust computerized maintenance management system (CMMS) can help optimize inventory by tracking what’s in stock and where it’s located, as well as anticipate supply chain lead time. A CMMS can track an item’s historical MTTF and also leverage it to manage inventory effectively.
MTTF Challenges: How to Overcome Them
Like any other maintenance metric, MTTF presents challenges. For example, maintenance teams can find it challenging to calculate MTTF accurately. Knowing the MTTF formula alone isn’t enough; it’s important to have the right tools, infrastructure, and strategies in place to collect and analyze MTTF data.
Lack of Data for the MTTF Formula
The Mean Time to Failure formula requires a large quantity of data to be accurate; a random sampling of machine failures won’t produce reliable results. For an accurate result when you calculate MTTF, you’ll need a large sample of asset operating times and the number of failures.
Asset Lifecycle Challenges When You Calculate MTTF
MTTF can be difficult to calculate for assets with a longer lifespan. If an asset can run for a long time before breaking down, that makes it challenging to calculate its mean time to failure.
Inconsistent Failure Rates Impact the MTTF Formula
MTTF assumes that assets always fail at a consistent rate. Unfortunately, this isn’t always true. In the real world, conditions are constantly changing; new machines, changing schedules, and environmental factors all impact asset performance and failure rates.
Overcoming Challenges When You Calculate MTTF
MTTF is an important metric, but you should never rely on just one metric or KPI. It’s a best practice to track MTTF along with MTBF (mean time between failures) and MTTR (mean time to repair). That will give you a more complete picture of your asset health.
Using a good CMMS can solve your data collection woes. A CMMS also makes it much easier to track a wide range of maintenance KPIs and metrics.
Aprender las diferencias: MTTF, MTBF, MTTR, FIT, MTTFd
There are many different metrics that help measure maintenance effectiveness. MTTF is just one of these valuable snapshots into your operations and is fundamental to helping you track and respond to assets that can’t be repaired. But what about repairable assets? There are a number of key metrics that you’ll need to consider. Two of the most important are Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR). MTBF depicts the expected time between two failures in a repairable system. Meanwhile, MTTR measures the average time it takes to fix a failed asset, including the time it takes to test and diagnose.
¿Qué es la diferencia entre MTBF y MTTF?
Tanto el MTBF como el MTTF son métricas útiles. Proporcionan información valiosa que su equipo puede utilizar para desarrollar e iterar en los procedimientos de mantenimiento, garantizando la mejora continua de los procesos en toda la planta.
MTBF, or Mean Time Between Failure, is used for repairable assets. This value tells you the average number of hours an asset can operate at best efforts before it needs maintenance. The length of time measured by MTBF is not the time between complete breakdowns. Instead, it’s the amount of time before the asset has to be repaired to operate optimally.
For example, if an asset is still operable but works more slowly than expected or allowed by your company’s best practices, it has reached failure since it needs to be repaired.
El MTTF, o tiempo medio hasta el fallo, se utiliza para los activos que deben sustituirse. Estos activos pueden ser completamente irreparables, o simplemente llevar demasiado tiempo y esfuerzo repararlos, lo que significa que su empresa optará por sustituirlos.
Por ejemplo, probablemente no calcularía el MTTF de una pieza reparable de un equipo de fabricación con una vida útil estimada de 15 años. Sin embargo, sí calcularía el MTTF de una correa de ventilador que el equipo de fabricación necesita para funcionar de forma óptima. Es posible que esta pieza deba sustituirse docenas de veces a lo largo de la vida útil del activo.
While you wouldn’t calculate MTTF for larger assets, you would calculate Mean Time Between Failures. This would show you how often the equipment needs to be repaired and can give you useful information that will help your maintenance team make decisions about preventive maintenance. While the asset will probably need to be replaced eventually, it can be kept in good working order by repairing it as needed.
Cómo utilizar MTTF
Tracking MTTF for common items like light bulbs and ball bearings can sound daunting. Correctly calculating this metric requires good recordkeeping, which can translate to lots of paperwork or time spent tracking data in a digital spreadsheet.
However, a CMMS can help you by automatically tracking these metrics. With the help of wirelessly connected sensors, integrated inventory management, and smart software, a comprehensive CMMS like eMaint can generate reports on demand so you have all the MTTF data you need at your fingertips.
Cuándo utilizar el MTTF
Una vez que haya calculado el MTTF de determinadas piezas, podrá utilizarlo cuando:
- Making purchasing decisions: Knowing each part’s MTTF can help you decide where to order parts.
- Adjusting maintenance procedures: MTTF scores can help you decide whether to adjust maintenance procedures to increase MTTF for certain parts that may have extendable lifespans.
- Creating operations budgets: Knowing MTTF for each part can help you estimate how much you’ll spend on parts over a given period of time.
- Developing inventory strategy: When you know the average lifespan of spare parts, you can decide whether you need to keep them on hand and how often you should order them.
KPIs and failure metrics like MTTF are key to measuring the success of maintenance programs. Manufacturing plants must be more productive than ever before to stay competitive. To keep up with ever-increasing demand, maintenance and reliability teams are overhauling their old practices and adopting new ones to minimize the impact of equipment failure on production timelines
Example of MTTF Calculation in Action
To understand how MTTF can guide real maintenance decisions, let’s look at an example.
A maintenance team wants to calculate the Mean Time to Failure (MTTF) for a batch of 10 identical ball bearings used in a conveyor system. Over time, they record how long each bearing operates before failure:
| Bearing | Hours Before Failure |
| 1 | 3,800 |
| 2 | 4,100 |
| 3 | 3,900 |
| 4 | 4,000 |
| 5 | 4,200 |
| 6 | 3,700 |
| 7 | 4,100 |
| 8 | 4,000 |
| 9 | 3,900 |
| 10 | 4,100 |
To calculate MTTF, add up the total operating hours for all failed bearings, then divide by the number of failures:
MTTF = (40,800 hours) ÷ 10 = 4,080 hours
On average, each bearing operates for about 4,080 hours before it fails. If conveyors run 24/7, that means each ball bearing will last about 5 ½ months. Using this information, the maintenance team can make key operational decisions.
- Inventory Planning: Knowing the average failure rate helps teams predict how many replacements they’ll need over a given time frame. Keeping at least a few on-hand spares for each conveyor line ensures maintenance can replace failed bearings quickly. This also ensures teams don’t over-buy inventory, freeing resources up for other uses.
- Preventive Replacements: If the cost of downtime outweighs the cost of parts, the team might choose to replace bearings before they reach the average failure time. For example, they may decide to replace a bearing at 3,700 hours while the conveyor is already down for maintenance, minimizing downtime. This approach also helps prevent unexpected breakdowns, especially in critical production lines.
- Improve Reliability Over Time: By tracking MTTF through a CMMS, maintenance teams can see if their maintenance actions, such as improved lubrication, better alignment, or vibration monitoring, extend component life. Over time, rising MTTF values show that reliability initiatives are working.
- Monitor Variability: While the MTTF is 4,080 hours, individual bearings failed between 3,700 and 4,200 hours. Analyzing this variability can help identify outliers, such as defective components or improper installation, to improve overall performance.
When consistently captured in a CMMS, MTTF data becomes a decision-making tool that helps balance cost, reliability, and uptime.
Cómo mejorar su MTTF
La mejora del MTTF implica varias estrategias destinadas a aumentar la longevidad y fiabilidad de los componentes no reparables. He aquí algunos métodos eficaces:
- Quality Control: Implementing stringent quality control measures during the manufacturing process ensures that components meet high standards. Regular inspections and testing can help identify defects early, reducing the likelihood of premature failures.
- Material Selection: Using high-quality materials that can withstand operational stresses and environmental conditions significantly enhances the durability of components. Material engineering plays a crucial role in extending the lifespan of products.
- Design Improvements: Optimizing the design of components to minimize stress concentrations and improve overall robustness can lead to longer MTTF. This includes considering factors like load distribution, thermal management, and corrosion resistance.
- Environmental Control: Protecting components from harsh environmental conditions, such as extreme temperatures, humidity, and exposure to chemicals, helps in maintaining their functionality over a longer period. This can be achieved through proper housing, coatings, and environmental controls.
- Predictive Maintenance: Using predictive maintenance techniques, such as condition monitoring and data analysis, allows for early detection of potential issues. This proactive approach helps in replacing components before they fail, thereby extending their effective operational life.
- Training and Awareness: Educating employees about best practices in handling, operating, and maintaining equipment ensures that components are used correctly and are less likely to experience premature failures.
Accessing the right data is the first step in improving MTTF, and the best resource for gathering and analyzing it is a CMMS. The past two decades have seen an increase in the use of remote condition monitoring technology to help predict and prevent unexpected failures. With access to more asset health data, via power monitoring and vibration monitoring, maintenance teams can gain a clearer picture of their efficiency and the general condition of a manufacturing plant’s machinery.
A robust CMMS makes all of this information readily available in one location, giving you the tools you need to optimize maintenance practices across your organization. If you’re in the market for an award-winning CMMS platform that can track MTTF and other KPIs, reach out to the experts at eMaint.