Herramientas de mantenimiento predictivo y la mejor forma de utilizarlas

Tiempo aproximado de lectura: 5 minutos

¿Qué es mantenimiento predictivo?

El mantenimiento predictivo (PdM) es una estrategia de mantenimiento que utiliza tecnología como sensores para supervisar el rendimiento y el estado de los equipos durante su funcionamiento normal. Esta información proporciona a los equipos de mantenimiento indicaciones tempranas cuando el activo experimenta un problema, antes de que se produzca el fallo. Cuando un equipo de mantenimiento conoce el estado de cada activo en tiempo real, puede tomar medidas de mantenimiento proactivas para reducir las posibilidades de fallos inesperados y tiempos de inactividad no planificados.

Traditionally, most maintenance teams have used reactive or preventive maintenance (PM) strategies, where repairs happen after machines fail or teams perform maintenance regularly based on the manufacturer’s guidelines. Today, many organizations use new predictive maintenance technologies — like IoT predictive maintenance sensors — to move beyond these methods and adopt a predictive maintenance approach.

Las organizaciones que utilizan software y herramientas de mantenimiento predictivo supervisan y comprueban características específicas para identificar cambios condicionales a medida que se producen. Existen numerosos métodos de prueba que se engloban en el mantenimiento predictivo, como las pruebas de infrarrojos, el análisis de vibraciones o el análisis de aceite, entre otros.

There is not one singular best maintenance solution, and assets within the same facility may benefit from different maintenance strategies. But for assets that are critical to the organization, predictive maintenance, also known as PdM maintenance, is often the best approach.

6 Steps for Establishing a Predictive Maintenance Program

Your ideal predictive maintenance solution depends on your organization’s size and budget, as well as your equipment and crew. However, there are a few key steps that every organization needs to follow to reap the full benefits of predictive maintenance, as illustrated in the graphic above.

1. Identify which assets to target for your predictive maintenance analytics program
   Not every asset is a great choice for a predictive maintenance approach. Inexpensive, easy-to-replace equipment probably doesn’t require a predictive maintenance solution, for example. Instead, identify your most critical assets — the ones you rely on to keep your production or service delivery running smoothly.This shouldn’t be guesswork; your CMMS holds the data you need to conduct a detailed criticality analysis. Work order histories, maintenance records, and KPIs like Mean Time Before Failure (MTBF) can give you insights into which assets need frequent repairs, as well as the associated costs. This data will paint a picture of which assets would benefit from a structured predictive maintenance solution.

2. Choose Your Predictive Maintenance Tools and Methods
   Condition monitoring is a key step on the path to predictive maintenance. Many organizations are already using IoT predictive maintenance tools to monitor vibration data, temperature, oil quality, and more.If you’re not already doing so, build a network of IoT predictive maintenance tools, like wireless sensors, to collect condition monitoring data in real time. The sensors will stream data to the cloud for predictive maintenance analytics.

   Decide which tools and methods match your organization’s needs. Depending on your assets and infrastructure, you may get the most relevant data from vibration monitoring, infrared thermography, or a different approach altogether.

3. Select and Train a Team
   An effective PdM maintenance team should include data scientists capable of constructing predictive models and managing your data infrastructure — that’s in addition to maintenance and operations experts and technicians who can use IoT predictive maintenance tools.Many organizations choose to outsource some or all PdM maintenance tasks by partnering with trusted experts in the field. It’s also a good idea to include AI tools to streamline the analytic process.

4. Perform System Integrations
   At this stage, integrate your IIoT predictive maintenance sensors and any other condition monitoring tools with your existing data-gathering systems, like SCADA and BI. The purpose is to produce one comprehensive stream of data that can be analyzed by your predictive maintenance model.

5. Coordinate Your Overall Maintenance Strategy
   Most organizations employ a mix of different maintenance approaches, including preventive maintenance and elements of reactive maintenance. Your CMMS can help coordinate all these approaches. CMMS software collects and stores data from IoT predictive maintenance sensors; it also handles processes like scheduling, generating work orders, and tracking task completion, so that even the most complex maintenance strategy can flow seamlessly.

6. Determine How To Share Asset Health Data
   It’s a good practice to standardize data collection methods, naming conventions, and maintenance metrics across your organization. This makes asset health data more meaningful and facilitates sharing. Using a CMMS enables instant access to data, even for remote teams. Manage permissions so that each team member has access to the data they need.

¿Cuáles son las Beneficios del mantenimiento predictivo?

The benefits of predictive maintenance go beyond the production floor. Not only does implementing a PdM maintenance solution make the workplace safer and production more efficient, but it also benefits the end users of the product and your organization’s bottom line.

Éstos son los principales Beneficios de utilizar herramientas de mantenimiento predictivo:

• Reduces unplanned downtime: When predictive maintenance software identifies a potential problem, teams can schedule maintenance during planned downtime. That way, the asset can continue to run as scheduled during normal hours.
• Safer work environment: Because planned maintenance is inherently less risky than reactive maintenance, predictive maintenance analytics creates a safer work environment. Catching failures early reduces the chance of injuries caused by unexpected machine malfunctions.
• Reduces the frequency of maintenance tasks: While preventive maintenance is a preferred strategy for many organizations, in some cases, it can lead to over-maintenance as teams perform unnecessary maintenance based on the manufacturer’s directions. With predictive maintenance, assets only receive maintenance when they need it, reducing costs and saving technicians time.
• Extends asset lifespans: Organizations invest substantially in their assets. So, increasing the availability and lifespan of those assets through predictive maintenance can drive maintenance KPIs and give organizations the best return on their investment.
• Lowers maintenance costs: It’s easier to correct smaller problems than to correct major failures. Predictive maintenance helps catch developing problems before they cause a full-blown shutdown or damage other parts of the equipment.
• Improves production quality: When machines aren’t running optimally, finished products are less likely to meet quality standards. Spotting and fixing issues early can reduce wasted materials, energy, and time.
• Supports data-driven maintenance decisions: If data gathered by sensors is stored in a cloud-based computerized maintenance management system (CMMS), teams can work together from wherever they are, consult with specialists, and make data-driven maintenance decisions based on predictive maintenance analytics.
• Improved work environment: With predictive maintenance, technicians can plan their work time to make the best use of their hours. Instead of rushing to fix assets after a breakdown they can plan maintenance as needed, lowering stress levels and minimizing unplanned downtime.

La gestión eficaz de los activos es crucial para las organizaciones en el entorno competitivo actual, y el mantenimiento predictivo proporciona a las organizaciones las herramientas para hacerlo con éxito. La mayor ventaja del mantenimiento predictivo es que aprovecha al máximo los recursos de mantenimiento.

What’s the Difference? Predictive Maintenance vs Preventive Maintenance

Preventive maintenance and PdM maintenance are both effective maintenance strategies, but there are key differences between the two. Understanding the differences between preventive and predictive maintenance can help your team select the best type of maintenance for your organization. In the same way, understanding the benefits of predictive maintenance and preventive maintenance can help you choose the right strategy. Many of the best maintenance programs use a combination of both strategies.

El mantenimiento preventivo utiliza el ciclo de vida previsto de un activo para determinar cuándo realizar las tareas de mantenimiento. Un ejemplo habitual de mantenimiento preventivo es cambiar el aceite de un coche cada tres meses o cada 5.000 km.

Un programa de mantenimiento preventivo es sencillo y suficiente para algunos activos. El mantenimiento preventivo de los activos puede realizarse en función del calendario, de un determinado número de horas de uso o de alguna otra métrica basada en el uso. Puede incluir tareas como el cambio de filtros, la lubricación o la sustitución de piezas desgastadas.

Of course, preventive maintenance presents some challenges. When the calendar dictates maintenance actions, some components are replaced before they need to be. There is also some risk incurred every time a machine is worked on. Preventive maintenance can be simpler to plan, but it uses more time, money, and parts.

Predictive maintenance uses the actual operating condition of an asset to determine what steps to take and when to take them. Instead of basing maintenance on a schedule, maintenance occurs when predictive maintenance analytics identify an irregularity in the asset’s performance. While similar steps, such as lubrication or parts replacement, may be taken, the difference is that predictive maintenance actions occur exactly at the time they are needed.

A predictive maintenance strategy can save both time and money, but it poses challenges, too: chiefly, the complexity of PdM maintenance implementation.  Fortunately, with the right tools, you can overcome this. While equipment is operating normally, it can be monitored by predictive maintenance technologies and condition monitoring devices, like remote sensors. They can take measurements at regular intervals or continuously.

Cuando se combinan con software de mantenimiento predictivo, estos sensores pueden alertar a los equipos de mantenimiento cuando cambia el estado de cualquier activo. Las órdenes de trabajo generadas automáticamente a través de un GMAO permiten a los equipos actuar con rapidez, evitando fallos en los equipos.

Los equipos de mantenimiento pueden rastrear y analizar los datos sobre el estado de los activos para ayudar a detectar patrones y tomar decisiones más informadas para el mantenimiento futuro. En última instancia, el objetivo del mantenimiento PdM es maximizar la disponibilidad de los activos y minimizar el tiempo y los costes de reparación.

How To Overcome Predictive Maintenance Challenges

Predictive maintenance comes with some built-in challenges. The program has a relatively high upfront cost, it requires managers to oversee complex operations, and it usually calls for training maintenance teams to use new technology. You can overcome these barriers if you implement your PdM maintenance program carefully.

It’s a good idea to start out with a pilot program, instead of trying to convert your whole organization to a predictive maintenance approach. Piloting the system lets you keep costs low, minimizes training, and limits the operation’s administrative requirements. It’s much more affordable to buy predictive maintenance technologies in small quantities, for example — and you’ll find that they quickly pay for themselves.

A successful pilot program will deliver a significant return on investment (ROI) that can then be invested in a larger PdM program. The pilot will also help drive understanding of predictive maintenance; maintenance crews will likely get on board with the new approach when they see results.

As we have seen, the right tools can also help you to overcome challenges. Using IoT sensors and high-quality data analytics allows you to meet the challenges of this highly complex maintenance approach.

What Are the 3 Types of Predictive Maintenance?

There are a number of different types of predictive maintenance. The most widely used types of predictive maintenance include vibration analysis, infrared thermography, and acoustic monitoring.

Análisis de vibración

Every rotating asset vibrates while in use. However, changes to an asset’s baseline vibration pattern usually indicate a new fault. Vibration analysis monitors an asset’s vibration levels in real-time, looking for anomalies.

Changes in vibration level can indicate premature wear and corrosion; they can also point to looseness, misalignment, and bearing faults.

Today, vibration analysis is highly sophisticated. Done right, the technique lets you spot machine faults months before they grow serious enough to cause a breakdown.

Acoustic Monitoring

Acoustic monitoring lets you — or rather, your condition monitoring tools — “hear” the early indicators of friction or wear and tear. Rotating equipment emits characteristic sounds as it deteriorates. Sometimes, those sounds are loud enough to hear with your naked ear, but acoustic monitoring catches much fainter sounds you can’t pick up, making it an excellent predictive tool.

Acoustic monitoring is widely used as a leak prevention tool, especially in systems with extensive pipelines for gas, oil, or liquids.

Infrared Cameras

Infrared cameras can detect subtle changes in temperature that may point to emerging machine faults.

Increases in temperature often result from high levels of friction, premature wear, or deterioration. Faulty wiring or other electrical issues are another possible root cause. Infrared thermography can also assist with locating gas or liquid leaks; it can spot changes in temperature caused by moisture or gas.

Of course, there are many other approaches to predictive maintenance. If you use a CMMS to anchor your predictive maintenance program, you’ll be able to integrate all of these different types of insights into one highly effective PdM model. 

Técnicas de mantenimiento predictivo

There are many ways to implement a predictive maintenance strategy, and many available predictive maintenance technologies.  The following predictive maintenance tools and techniques give each organization the power to gather as much or as little information as they need to implement and maintain their predictive maintenance program.

• Vibration monitoring: Sensors installed on equipment can monitor in-depth vibration readings. Once the baseline for the asset is established, these sensors can be continuously monitored to detect deviations that could indicate faults like imbalances, misalignments, or bearing faults.
• Temperature monitoring: Similar to vibration monitoring, sensors can detect when temperatures rise above the asset’s normal temperatures. When a temperature increase is detected, technicians can find and address the root cause before failure occurs.
• Condition monitoring: Using a cloud-based CMMS stores sensor data in the cloud, where it can be monitored and analyzed from anywhere. Even if equipment is in a remote location or monitoring needs to occur off-site, users can access current or historical data and use it to make decisions about maintenance and replacement.
• Artificial intelligence (AI) analysis and recommendations: Learning how to read the signatures provided by vibration sensors takes years of education and experience. Now, even if your organization doesn’t have an expert on-site, advanced AI-powered analytics can assess machine vibration patterns and identify changes. It can even recognize different patterns of common issues, giving your team the insight to find and fix the problem even faster.
• Alarms: When vibration levels indicate faults, predictive maintenance software can send alerts to the appropriate personnel so they can take immediate action.
• Automated work orders: If the vibration monitoring software is integrated with a computerized maintenance management system, the CMMS can automatically trigger a work order when a fault is detected, saving time and reducing the amount of human intervention needed to fix the problem.

Ejemplos de mantenimiento predictivo

Predictive maintenance tools and strategies can benefit assets in almost any industry. Here are just a few predictive maintenance examples from different industries.

Predictive Maintenance Examples in Automotive

Predictive maintenance tools can identify impending failures, such as a slowing conveyor belt or abnormalities in vibrations from stamping or press machines. It can also be used on other assets, like forklifts and painting equipment.

Predictive Maintenance Examples in Food and Beverage

In the food and beverage industry, predictive maintenance technologies can play a role in not only ensuring maximum uptime, but also ensuring all products are created in compliance with strict food regulations. Predictive maintenance can be used on equipment like mixers and blenders, dust collection systems, extrusion equipment, pumps, and conveyor belts.

Predictive Maintenance Examples in Manufacturing

Fabricantes de todo tipo pueden utilizar la tecnología de mantenimiento predictivo para mejorar la consistencia y calidad de sus productos, reducir los costes de mano de obra y prolongar la vida útil de los activos. El mantenimiento predictivo en la fabricación puede ayudar a predecir y reducir fallos en activos como ventiladores, bombas y motores.

Predictive Maintenance Examples in Life Sciences

Many manufacturers in the life sciences industry are subject to audits from local, state, and federal authorities. Predictive maintenance technologies can ensure equipment stays running within required parameters and can provide organizations with audit-proof records of asset history. And in cases where products need to be refrigerated or frozen, sensors help ensure that the equipment used to keep them at the proper temperature is always working as intended.

Predictive Maintenance Examples in Oil and Gas

La fiabilidad es increíblemente importante en la industria del petróleo y el gas, donde los fallos de los equipos pueden tener consecuencias medioambientales y suponer amenazas para la seguridad de los empleados. El mantenimiento predictivo de activos como bombas, calderas y compresores puede ayudar a reducir los riesgos de fallos imprevistos y sus consecuencias.

How To Create a PdM Maintenance Program

El cambio del mantenimiento reactivo al predictivo no se produce de la noche a la mañana. Pero los avances en las tecnologías de mantenimiento predictivo, como el software GMAO y los sensores de vibración inalámbricos, han hecho del mantenimiento predictivo una estrategia más alcanzable que nunca. Hay algunas cuestiones que deben tenerse en cuenta para cada activo a la hora de considerar la creación de un plan de mantenimiento predictivo:

• Si este activo falla, ¿cómo afecta a la producción?
• ¿Cuánto cuesta la reparación de este bien?
• ¿Cuánto cuesta reemplazar este activo?

Responder a estas preguntas para cada equipo puede ayudar a los equipos a determinar qué activos deben mantener de forma predictiva.

El mantenimiento predictivo no es necesariamente la estrategia más eficaz para todos los activos. Algunos activos pueden funcionar hasta el punto de fallar sin apenas afectar a la producción o a los resultados. Otros se benefician de un mantenimiento preventivo simple y directo. Pero para algunos activos, el mantenimiento predictivo es la mejor estrategia.

Incluso si planea utilizar herramientas de mantenimiento predictivo en sólo un puñado de activos, es útil planificar con antelación y crear un programa que su equipo de mantenimiento pueda cumplir. He aquí seis pasos clave para establecer su programa de mantenimiento predictivo:

1. Identificar qué activos deben ser objeto de mantenimiento predictivo
2. Elegir las herramientas y métodos de mantenimiento predictivo que utilizará para supervisar el estado de los activos (como sensores y un GMAO).
3. Seleccionar y formar a un equipo de implantación para aprender y llevar a cabo tecnologías de mantenimiento predictivo.
4. Realizar integraciones de sistemas para obtener una imagen completa del estado de los activos
5. Coordinar su estrategia global de mantenimiento, identificando qué enfoque se utilizará en cada caso
6. Determinar cómo se compartirán los datos sobre la salud de los activos entre los miembros del equipo, las partes interesadas y los auditores

En última instancia, para implantar con éxito un programa de mantenimiento predictivo es necesario tener una visión a largo plazo de los objetivos y necesidades de su organización. No hay dos planes de mantenimiento predictivo iguales.

¿Cómo controlar el mantenimiento predictivo?

Predictive maintenance, by definition, involves collecting and analyzing a lot of data. The best way to control predictive maintenance is by using a computerized maintenance management system (CMMS) to connect and manage data coming in from work orders, real-time predictive maintenance analytics, and maintenance history, making it accessible to the appropriate personnel no matter where or when they’re working.

Sin un GMAO, los equipos de mantenimiento a menudo tienen que adivinar el historial de mantenimiento de un activo. Las órdenes de trabajo suelen estar en papel, y lleva tiempo encontrarlas, completarlas y archivarlas. Las órdenes de trabajo en papel también dificultan el seguimiento de lo que se ha completado o está pendiente. Es casi imposible comparar toda la gama de solicitudes, tareas en curso y trabajos prioritarios cuando están todos en hojas de papel separadas.

Un GMAO hace que las órdenes de trabajo sean mucho más fáciles de programar, asignar y completar. Las órdenes de trabajo también pueden priorizarse en función de la criticidad del activo, lo que garantiza que las tareas más importantes se asignen a los técnicos adecuados. Los gestores pueden ver qué tareas están pendientes y asignar los trabajos al personal que ya está trabajando en un activo específico o a aquellos con la experiencia necesaria para la tarea.

Technicians and decision-makers will also have access to historical maintenance records. When an asset has a history of multiple failures in a short time frame, experts can use the data and predictive maintenance analytics to get to the root cause of the issue or decide if it’s time to replace the asset.

Características del software de mantenimiento predictivo eMaint

eMaint GMAO ofrece a las empresas un conjunto completo de herramientas de mantenimiento predictivo. Con él, las organizaciones pueden:

• Definir las clases de vigilancia para cada activo
• Supervise el ruido, las vibraciones, la temperatura, los lubricantes, el desgaste, la corrosión, la presión y el flujo de forma independiente
• Introducir manualmente o importar las lecturas de los medidores
• Definir los límites superior e inferior de la operación aceptable para cada activo
• Visualización de las lecturas en forma de informe con excepciones codificadas por colores
• Autodisparo de correos electrónicos cuando se supera un límite
• Autogenerar órdenes de trabajo cuando una lectura se sale de los límites predefinidos
• Realizar análisis de datos para identificar fallos de forma temprana, prevenir averías y optimizar los recursos de mantenimiento.
• Ver diagrama monitoreo de condición

Estudio de caso: Utilización de eMaint GMAO monitoreo de condición para el Mantenimiento Predictivo

Cleveland Tubing, Inc. is a manufacturer of flexible, collapsible tubing products, including FLEX-Drain and PumpFlex. The company set up eMaint so that meter readings on key indicators (temperature, pressure, fluid levels, suction) are imported and used to trigger priority work orders when work or inspection is needed based on predefined ranges.
Gary Payne, maintenance manager for Cleveland Tubing, noted that eMaint has become their maintenance decision support system, informing them of the tasks that need to be performed each day, based on elapsed time, equipment utilization and condition-based indicators. They also experienced:

• Informes automatizados para reponer el inventario de piezas almacenadas y no almacenadas
• Seguimiento de tiempo de trabajo racionalizado para el departamento de cinco empleados de mantenimiento
• Mejores cálculos de retorno de la inversión con una mejor asignación de los costos de mano de obra y materiales a los activos
• The ability to evolve from reactive maintenance to planned maintenance to predictive maintenance via condition monitoring and automated alerts of potential problems on critical equipment
• Easily measure and track KPIs against world-class standards (90% planned maintenance)

Learn More About CMMS

¿Qué industrias utilizan el mantenimiento predictivo?

El mantenimiento predictivo es una estrategia útil para una amplia gama de industrias. Aprovecha tecnologías y herramientas -desde sensores hasta software GMAO y análisis estadísticos- para reducir los tiempos de inactividad imprevistos y el despilfarro de recursos.

Any organization seeking to extend the lifespan of its assets and optimize maintenance spending can use predictive maintenance.

El software de mantenimiento predictivo eMaint da servicio a clientes de industrias como:

• Manufactura
• Alimentación y bebidas
• Gobierno
• Sanidad (incluyendo productos farmacéuticos, dispositivos médicos, etc.)
• Energía (incluido el petróleo y el gas, la energía eólica, etc.)
• Educación
• Almacenamiento y distribución
• Transporte y flota
• Instalaciones

¿Cuáles son los Beneficios del mantenimiento predictivo?

El mantenimiento predictivo es una estrategia de mantenimiento rentable con numerosos Beneficios. Entre ellos

• Evitar los tiempos de inactividad imprevistos
• Mejorar la productividad
• Prolongar la vida útil de los activos y maximizar el tiempo entre compras
• Reducción de la cantidad de materiales y repuestos necesarios
• Crear un entorno de trabajo más seguro
• Beneficio para la cuenta de resultados

eMaint GMAO
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