Case Study Predictive Maintenance
Reducing downtime by detecting failures before they occur
Maintenance should not start when the machine has already failed.
Industry
Automobile manufacturer
90%
accuracy in failure prediction

-65%
Unplanned stoppages in production lines

-40%
Costs associated with corrective maintenance

+30%
Operational availability of critical machinery

90%
Accuracy in predicting device failures
BEFORE
The faults were detected when the machine had already stopped operating
Unexpected stoppages affecting production and deliveries
High costs due to incidents and urgent replacement of components
NOW
Automatic alerts before failure occurrence with 90% accuracy
Continuous vibration, temperature and performance monitoring
Maintenance planning without stopping production

THE PROBLEM
Faults were detected too late.
- Failures detected when the impact was already critical.
- Time lost identifying the cause of the problem.
- Maintenance based on inefficient periodic revisions.
- Recurrent incidents in key production equipment.

THE SOLUTION
Automatic prediction of industrial incidents.
- Continuous data capture from industrial sensors and PLCs.
- Predictive models trained to detect anomalies.
- Automatic alerts when a component shows risk of failure.
- Real-time visualization of machine status.

THE IMPACT
Fewer incidents.
More operational continuity.
- Drastic reduction of unexpected interruptions.
- Better planning of technical resources and production.
- Longer useful life of industrial equipment.
- Prioritization of maintenance according to operational impact.
Apply predictive maintenance
in your organization.
Detect incidents before they affect production.
From reacting to failures to anticipating them
The information already existed in the machines. Now it allows action to be taken before the problem occurs.
More operational continuity, fewer incidents and more efficient production.