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. Predictive maintenance uses historical and real-time data from various parts of your operation to anticipate problems before they happen.

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.

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.