Predictive maintenance

Everything you need to know about predictive maintenance

The predictive maintenance starter kit

What is predictive maintenance?

Predictive maintenance is a technique that uses condition-monitoring tools and techniques to track the performance of equipment during normal operation to detect possible defects and fix them before they result in failure.

Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance.

How does predictive maintenance work?

Predictive maintenance uses condition-monitoring equipment to evaluate an asset’s performance in real-time. A key element in this process is the Internet of Things (IoT). IoT allows for different assets and systems to connect, work together, and share, analyze and action data.

IoT relies on predictive maintenance sensors to capture information, make sense of it and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.

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.

Benefits of predictive maintenance

When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when it is required. That is, just before failure is likely to occur. 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

Predictive maintenance programs have been shown to lead to a tenfold increase in ROI, a 25%-30% reduction in maintenance costs, a 70%-75% decrease of breakdowns and a 35%-45% reduction in downtime.

These cost savings come at a price, however. Some condition monitoring techniques are expensive and require specialist and experienced personnel for data analysis to be effective.

What is PdM suitable for?

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Suitable applications

Applications that are suitable for predictive maintenance 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

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.

Disadvantages of predictive maintenance

Compared with preventive maintenance, the cost of the condition monitoring equipment needed for predictive maintenance is often high. The skill level and experience required to accurately interpret condition monitoring data is also high. Combined, these can mean that condition monitoring has a high upfront cost. Some companies engage condition monitoring contractors to minimize the upfront costs of a condition monitoring program.

Not all assets have failures that may be more cost-effectively maintained using preventive maintenance or a run-to-failure maintenance strategy. Judgment should be exercised when deciding if predictive maintenance is best for a particular asset. Techniques such as reliability-centred maintenance provide a systematic method for determining if predictive maintenance is a good choice as an asset maintenance strategy for the particular asset of interest.

The bottom line: the impact of predictive maintenance

Predictive maintenance seeks to define the best time to do work on an asset so maintenance frequency is as low as possible and reliability is as high as possible without unnecessary costs.

Utilizing the Internet of Things is key for implementing a successful predictive maintenance program, as is the use of predictive maintenance sensors and techniques, such as vibration analysis, oil analysis, thermal imaging, and equipment observation.

Although there are some disadvantages to predictive maintenance (high start-up costs, the need for specialized skills, the limitations of some equipment), it allows maintenance to be performed only when required, helping facilities cut costs, save time and maximize resources. Consultation with equipment manufacturers and condition monitoring experts should be undertaken before deciding if predictive maintenance is best for particular assets.

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