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AI enabling true predictive maintenance

AI enabling true predictive maintenance

Predictive maintenance is a proactive approach to maintaining industrial electrical assets that involves using advanced technologies, such as machine learning (a form of Artificial Intelligence), to predict when a piece of equipment is likely to fail. This allows maintenance teams to address problems before they occur, rather than waiting for scheduled maintenance or for the equipment to fail. In contrast, scheduled maintenance is a reactive approach that relies on fixed intervals for maintenance, regardless of the actual condition of the equipment.

There are several key advantages of predictive maintenance over scheduled maintenance:

Reduced maintenance costs

Scheduled maintenance requires maintenance teams to perform work on equipment regardless of whether it is actually necessary. This can lead to unnecessary spending on parts and labour. Predictive maintenance, on the other hand, allows maintenance teams to focus their efforts on equipment that actually needs attention, reducing costs and increasing the overall efficiency of the maintenance program.

Increased equipment uptime

By identifying potential problems before they occur, predictive maintenance allows maintenance teams to address issues before they lead to equipment failures. This helps to reduce downtime and increase the amount of time that equipment is able to operate.

Improved safety

Predictive maintenance can help identify safety hazards and potential risks before they occur, which can help prevent accidents and injuries.

Better decision-making

By using sensor data and machine learning, predictive maintenance provides maintenance teams with valuable insights into the condition of equipment. This allows teams to make data-driven decisions about when and how to maintain equipment, which can help improve overall equipment performance and reduce maintenance costs.

Better data collection, monitoring and inspection

AI-based systems such as Numen, can be used to track the asset health and predict the failure before it occurs, this way maintenance teams can do the required maintenance just in time

In conclusion, predictive maintenance is a proactive approach that helps industrial organisations increase equipment uptime, reduce maintenance costs, improve safety, and make better decisions. By using advanced technologies like machine learning and sensor data, organisations can gain a deeper understanding of their equipment and predict when maintenance is actually needed, leading to a more efficient and cost-effective maintenance program.