What is Predictive Maintenance (PdM)? Its Pros and Cons

For manufacturers, one of the top priorities is to maximise the machinery life cycle. As sudden machinery breakdowns can lead to downtime and expensive reparation. Consequently, manufacturing businesses have to look for a solution to reduce the maintenance cost and increase the equipment uptime and availability.

Typically, the manufacturers would turn to preventative maintenance – to regularly inspecting equipment and tuning them up, whether they need it or not. However, preventative maintenance is not based on the actual condition of the equipment, and hence can sometimes be unnecessary and wasteful. Predictive maintenance, on the other hand, provide a solution to make maintenance much more efficient and cost-effective.

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Defining predictive maintenance

Predictive maintenance is the practice of monitoring the actual condition of the equipment to predict when failures may occur and performing maintenance before the equipment breaks down. When applying predictive maintenance, manufacturers can reduce the maintenance costs by minimising the frequency of maintenance needs, reducing unplanned breakdowns, and eliminating unnecessary preventive maintenance.

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In details, predictive maintenance (PdM), rooted in predictive analytics, utilises data from multiple sources, namely critical equipment sensors, enterprise resource planning (ERP) systems, computerised maintenance management systems (CMMS), and production data. With real-time insights and continuous monitoring, the equipment failure patterns or anomalies will be detected in the early stage so the maintenance managers can allocate their resources more efficiently and effectively.

How predictive maintenance works

PdM’s main purpose is to predict when equipment failure might occur and provides insights that support the planning process for machinery maintenance. Leveraging the Internet of Things technology - wireless sensors, data is collected and analysed to reveal real-time operation status. There are different types of data to collect, each tracks different features of the production chain - from temperature to vibrations and ultrasonic detection.

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To predict potential machine failures, manufacturers have to work out a predictive technique that works best for them. The chosen best technique not only has to predict failures but also be able to provide sufficient warning time for upcoming maintenance. It takes both hardware to monitor the equipment and software to propose the corrective work order.

Predictive maintenance techniques consist of:

• Vibration analysis: For heavy-duty machinery, manufacturers can use vibration sensors to spot out degradation in performance. For example, the shafts and bearings in pumps and motors will vibrate differently when they are worn out. It is said that vibration analysis is one of the most accurate techniques for identifying functional issues in machines.
• Thermal imaging: Also known as the infrared technique, this test will detect hot spots in equipment while it’s in use, meaning there is too much friction on those parts. The findings usually alert manufacturers with potential issues that require repair.
• Sonic and ultrasonic analysis: This technique uses sound cues to detect small cracks and failing welds before they’re visible and cause gas or liquid leaks.
• Oil analysis: Oil analysis checks the particles in machines that use oil. The more metal particles there is, the greater the signs of wear. Besides, this technique can also identify oil leaks and examine its cleanliness.

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Besides the techniques above, predictive maintenance can also apply emission testing and condition monitoring to deepen the performance analytics. With an appropriate combination of multiple techniques, integrating machine learning and additional tools such as CMMS, machine failures are minimised and maintenance workload is reduced. Hence, reduces the total time and budget spent on maintaining the equipment.

Disadvantages to consider beforehand

The application of predictive maintenance needs to be implemented carefully. It requires highly specialised skill level and extended expertise to accurately interpret the condition of monitoring data. Employees must be well-trained and must possess mixed experience in both IT and machinery.

Moreover, in comparison with preventative maintenance, applying the monitoring techniques can be costly in the beginning. This leads to some companies turning to condition monitoring contractors to minimise the upfront costs of a condition monitoring program.

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Before making a decision to implement predictive maintenance, manufacturers should consider their scale and priorities. If their organisation prioritises cost-effective methods, then predictive maintenance is the better choice compares to preventive maintenance. Although predictive maintenance has a high upfront cost, in the long-run, predictive maintenance can benefit your maintenance team as well as the entire organisation.