Predictive maintenance is a data-driven maintenance strategy that uses artificial intelligence and sensor technology to predict potential failures before they occur. Instead of relying on fixed maintenance intervals or reacting only after breakdowns, machines and systems are continuously monitored in real time. Relevant data such as vibration, temperature, pressure, or cycle counts, is collected by sensors, analyzed by AI algorithms and transformed into actionable insights.
This approach enables companies to detect anomalies, wear, or creeping deviations at an early stage. For example, pneumatic cylinders, electric drives, or compressors can be monitored permanently, with the system triggering alerts long before a malfunction would cause unplanned downtime. The result: targeted and condition-based maintenance instead of costly, reactive repairs.
In short, predictive maintenance shifts maintenance from a cost factor to a strategic advantage. With AI at the core, manufacturers can boost productivity, reduce risks, and remain competitive in the era of Industry 4.0.
By continuously monitoring the condition of machines and systems, critical events or creeping deviations can be detected at an early stage. Sensors on your machines and systems collect data that is then forwarded to the system or artificial intelligence.
Based on the determined values and calculations, the AI suggests appropriate maintenance and repair measures.
Predictive maintenance is the key to reducing downtime, cutting costs, and boosting equipment performance. But without the right tools and strategy, manufacturers risk unexpected failures, inefficient repairs, and lost productivity.
In this whitepaper, you’ll discover:
✅How to increase overall equipment effectiveness and total performance maintenance without major costs and effort
✅The role of predictive maintenance
✅Data and AI - the revolution in maintenance
✅From theory to practice: reduced downtimes on machine tools with AI
Download now and take the first step toward smarter, AI-driven equipment performance.