Case Study Predictive Maintenance

Reducing downtime by detecting failures before they occur

Maintenance should not start when the machine has already failed.

BismartIcon_Fabrica

Industry
Automobile manufacturer

90%
accuracy in failure prediction

Bismart_Refresh configuration

-65%
Unplanned stoppages in production lines

Bismart_Profit-215

-40%
Costs associated with corrective maintenance

Bismart_Connection settings

+30%
Operational availability of critical machinery

Bismart_Analysis data

90%
Accuracy in predicting device failures

BEFORE

BismartIcon_Negative

The faults were detected when the machine had already stopped operating

BismartIcon_Negative

Unexpected stoppages affecting production and deliveries

BismartIcon_Negative

High costs due to incidents and urgent replacement of components

NOW

Bismart-18 Consulting Kit

Automatic alerts before failure occurrence with 90% accuracy

Bismart-18 Consulting Kit

Continuous vibration, temperature and performance monitoring

Bismart-18 Consulting Kit

Maintenance planning without stopping production

Bismart_Puzzle

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.
Bismart_Our solutions

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.
Bismart_Focus

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.

Bismart Icon_Transform

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.