What is predictive maintenance (PdM)?

Predictive maintenance (PdM) uses data analysis to identify operational anomalies and potential equipment defects, enabling timely repairs before failures occur. It aims to minimize maintenance frequency, avoiding unplanned outages and unnecessary preventive maintenance costs.

How does predictive maintenance work?

Predictive maintenance uses historical and real-time data from various parts of your operation to anticipate problems before they happen. There are three main areas of your organization that factor into predictive maintenance:

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Predictive maintenance relies heavily on technology and software, particularly the integration of IoT, artificial intelligence, and integrated systems. These systems connect various assets, enabling data sharing, analysis, and actionable insights. Information is gathered through sensors, industrial controls, and business software like EAM and ERP. This data is then processed to pinpoint areas needing attention, with techniques such as vibration analysis, oil analysis, thermal imaging, and equipment observation serving as examples.

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Choosing the correct technique for performing condition monitoring is an important consideration that is best done in consultation with equipment manufacturers and condition monitoring experts.

When is predictive maintenance suitable?

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Suitable applications

Applications that are suitable for predictive maintenance (PdM) include those that:

  • Have a critical operational function
  • Have failure modes that can be cost-effectively predicted with regular monitoring
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Unsuitable applications

Unsuitable applications for predictive maintenance include those that:

  • Do not serve a critical function
  • Do not have a failure mode that can be cost-effectively predicted

See if an asset is a match for PdM with this asset criticality analysis template

Who uses predictive maintenance?

Generally speaking, a maintenance manager and maintenance team use predictive maintenance tools and asset management systems to monitor impending equipment failure and maintenance tasks.

How is predictive maintenance used?

Let's say you have a pump on your production line. If this pump breaks, it will stall production until you can fix or replace it, which could take hours. Your asset management system can monitor the pump’s temperature. If its temperature rises past a certain threshold, you know the pump is under stress and could possibly fail soon. You can then schedule some time to perform preventive maintenance before a complete failure stops production.

Predictive maintenance software can notify the maintenance team of the stress on a specific machine. It uses predictive analytics to flag issues and lets the team know to set up preventative maintenance, which helps reduce costly downtime.

Advantages of predictive maintenance

Compared with preventive maintenance, predictive maintenance ensures that a piece of equipment requiring maintenance is only shut down right before imminent failure. This reduces the total time and cost spent maintaining equipment.

This brings several cost savings:

  • Minimizing the time, the equipment is being maintained
  • Minimizing the production hours lost to maintenance
  • Minimizing the cost of spare parts and supplies

In addition to these advantages predictive maintenance also:

  • Increase asset life: Regularly monitoring equipment health and addressing minor issues before they become major can extend the useful life of assets.
  • Optimizes maintenance activities: Instead of routine or scheduled maintenance (which might be over or underdone), PdM ensures maintenance is carried out only when required, leading to efficient use of resources.
  • Allows for better spare parts management: Knowing in advance what parts might fail allows for better inventory management, reducing the need for overstocking and ensuring parts are available when needed.

Disadvantages of predictive maintenance

By using predictive techniques, maintenance can be performed just in time to avoid unplanned downtime and improve equipment lifespan. While there are many advantages to this approach, there are also some disadvantages to consider:

  • High initial costs: Setting up predictive maintenance typically requires investments in sensors, data analytics software, and sometimes even IoT (Internet of Things) infrastructure. For many companies, the upfront costs can be quite high.
  • Complexity: Implementing predictive maintenance requires integrating different technologies and systems, analyzing vast amounts of data, and retraining personnel. This can introduce complexities that not every organization is equipped to handle.
  • Over-reliance on technology: Just because the system predicts that a piece of equipment is fine doesn't always mean it is. There's always a risk of becoming too reliant on predictive data and ignoring other signs of equipment problems.

Predictive vs. preventive maintenance

Predictive maintenance Preventive maintenance
  • Is proactive maintenance
  • Uses predictive technology to address potential problems and schedule corrective maintenance before a failure
  • Focuses on asset performance, predictive analytics, and data collection for services on machinery
  • Improves overall inventory efficiency since machine parts are not run to failure and are not replaced too soon
  • Does not often require machine downtime, and if it does, it’s generally short
  • Uses advanced tools like vibration analysis, infrared thermography, oil analysis, combined with data analytics software and sometimes sensor devices
  • Is planned maintenance
  • Often utilizes scheduling software to notify teams or individuals of upcoming equipment maintenance
  • For example, when a car reaches a certain number of kilometers traveled it will notify the driver for an oil change
  • Gives you good indicators of asset performance and asset health
  • Often requires machine downtime
  • Typically relies on maintenance checklists, manuals, and standard operating procedures.

The impact of predictive maintenance

Predictive maintenance optimizes the timing of work on assets to minimize frequency and maximize reliability without added costs. It leverages sensor data, AI, and machine learning to guide maintenance decisions. A successful predictive maintenance program crucially depends on techniques like vibration analysis, and equipment observation. Despite challenges like high initial costs and the need for specialized expertise, it's efficient, saving costs and resources. Before adopting predictive maintenance, consult with equipment manufacturers and monitoring experts.

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