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ITA Predictive Maintenance with AI: Is It Really Possible?
Maintenance has traditionally been either corrective (fixing issues after they occur) or scheduled (interventions based on predefined time intervals). Both approaches can result in unnecessary costs, downtime or premature component replacement.
Today, more and more organizations are exploring predictive maintenance: the ability to anticipate failures before they happen by analyzing operational data.
But does Artificial Intelligence truly make this possible?
Yes — provided that the right data is available.
Predictive maintenance relies on the continuous monitoring of equipment performance. IoT sensors, smart meters, PLCs and SCADA systems collect data such as vibration, temperature, pressure, electrical load and machine efficiency.
AI compares these patterns to historical failure models and identifies anomalies.
This makes it possible to:
- Detect failure risks early;
- Plan maintenance more efficiently;
- Reduce unplanned downtime;
- Extend asset lifetime;
- Lower operational and replacement costs.
AI does not replace maintenance technicians — it supports them with clear insights to guide decisions.
The key is having complete, reliable and continuous data. Without IoT monitoring and integration, AI cannot deliver predictive value.
UTwin combines AI, Digital Twin, BIM, CMMS and IoT into a single platform. It collects equipment data, analyzes performance and suggests predictive maintenance actions — reducing downtime and optimizing costs.
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