Case Study Predictive Maintenance
Reducing downtime by detecting failures before they happen
Maintenance should not start when a machine has 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.
Artificial Intelligence
How does predictive maintenance reduce unplanned downtime?
Predictive maintenance helps detect signs of failure before a machine stops working. By using data from industrial sensors, PLCs and predictive models, companies can anticipate incidents, plan maintenance interventions and avoid unexpected interruptions in the production line.
What data is used in an industrial predictive maintenance model?
An industrial predictive maintenance model can use vibration, temperature, performance, machine activity and other data captured by sensors or PLCs. This information helps identify abnormal patterns and estimate the risk of failure in critical components.
What are the benefits of anticipating machinery failures?
Anticipating machinery failures helps reduce unplanned downtime, lower corrective maintenance costs and improve the operational availability of critical equipment. It also makes it easier to plan technical resources and prevent failures from affecting production or delivery schedules.
How do automatic alerts work in predictive maintenance?
Automatic alerts are triggered when the system detects signs that a machine or component may be at risk of failure. These alerts help technical teams act before the failure occurs and prioritise maintenance based on urgency and operational impact.
Why is predictive maintenance important in automotive manufacturing?
In automotive manufacturing, an unexpected machine failure can affect the entire production chain. Predictive maintenance helps monitor critical equipment, reduce incidents, extend asset life and support a more continuous, efficient and reliable production process.