// Predictive Maintenance

wear under observation

Predictive maintenance with vision systems

Predictive maintenance with vision systems replaces traditional scheduling with a system based on real data.
A camera observes the condition of the component during operation and reports when it approaches the critical threshold.

Tool tips, blades, hooks, ropes, rollers and in general all elements under continuous stress. On a production line as well as on an operating vehicle.

Vision system for predictive maintenance

Wear monitoring

Continuous monitoring of tool tips, blades, hooks, cables and any element under stress.

Micro-damage

Inspection of the surface and strands to identify signs of failure before they become a risk.

Alerts and planning

Post-analysis alerts and custom software to assess and schedule maintenance interventions.

Wire ropes and cables

Surface and strand analysis

On winches and lifting equipment, the system follows the spooling in real time, recognizes cable positioning errors and inspects the strands for signs of wear, before they can turn into a risk.

The same principle applies to hooks, chains and gripping devices: the measurement is repeatable, it comes at every cycle and does not depend on the operator or on the time available.

Continuous production lines

Where the risk of breakage becomes machine downtime

On high-volume, high-speed machines and lines, the benefit is measured in avoided stoppages.
Scrap noticed too late, unnoticed breakages and material buildup are situations that can easily be avoided by installing a vision system.

The collected data feeds statistical reports that make it possible to optimize the line and avoid waste.

complete management of the implementation process

Observation, development and testing

The first phase of every project is collecting real data in the field: we capture images under real conditions, our developers label them, and the AI model is trained on them where needed.

Once installed and tested, the system is ready to use and upgradable.
If the cycle or the product changes, the dataset is adjusted: we work remotely, without stopping the plant longer than necessary.

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