For manufacturers, one of the top priorities is to maximise the machinery life cycle. Sudden machinery breakdowns can lead to downtime and expensive repairs. 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 inspect equipment and tune it up, whether it needs 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, provides 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 equipment condition to predict when failures may occur and performing maintenance before equipment breaks down. When applying predictive maintenance, manufacturers can reduce maintenance costs by minimising maintenance frequency, reducing unplanned breakdowns, and eliminating unnecessary preventive maintenance.
In detail, 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 provide 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 of which tracks different features of the production chain – from temperature to vibrations and ultrasonic detection.
To predict potential machine failures, manufacturers need to develop a predictive technique that best suits their needs. The chosen best technique not only has to predict failures but also has to 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 detect performance degradation. For example, the shafts and bearings in pumps and motors will vibrate differently as they wear. 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 detects hot spots in equipment while it’s in use, indicating excess friction on those parts. The findings usually alert manufacturers to potential issues that require repair.
- Sonic and ultrasonic analysis: This technique uses sound waves to detect small cracks and failing welds before they become visible and cause gas or liquid leaks.
- Oil analysis: Oil analysis checks the particles in machines that use oil. The more metal particles there are, the greater the signs of wear. Besides, this technique can also identify oil leaks and examine their 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 implementation of predictive maintenance needs to be carefully managed. It requires a highly specialised skill set and extensive expertise to accurately interpret the condition of the monitoring data. Employees must be well-trained and possess a mix of experience in both IT and machinery.
Moreover, compared with preventive maintenance, applying monitoring techniques can be costly initially. This leads some companies to turn to condition monitoring contractors to minimise the upfront costs of a condition monitoring program.
Before deciding 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 compared to preventive maintenance. Although predictive maintenance has a high upfront cost, in the long run, it can benefit your maintenance team and the entire organisation.
