Predictive Maintenance System

Data-driven predictive maintenance that reduces downtime by up to 40%

Maintenance should no longer be reactive

In many industrial operations, equipment fails without warning, causing delays, production loss, and high repair costs.

Most of these failures could be avoided if there was a way to anticipate them.

Predictive Maintenance offers precisely that: an intelligent cloud platform that analyzes operational data to predict failures, reduce downtime, and optimize equipment lifespan.

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What is data-driven predictive maintenance?

It's a modern approach that uses advanced analytics and artificial intelligence to:

• Detect unusual behaviors in equipment
• Identify early failure signals
• Estimate the ideal time to perform maintenance
• Reduce unnecessary interventions
• Avoid unexpected stops

Everything is processed in the cloud, without depending on complex infrastructure, and with the ability to feed from different data sources that the plant already handles.

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Leveraging existing data

Each company has its own methods for recording information about the status of its assets.

That's why the platform is designed to receive data from:

• Operational histories
• Vibration or temperature records
• Connected equipment parameters
• Existing internal systems
• Any available data flow

The goal is to convert that dispersed information into a clear and useful prediction tool.

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The AI module: anticipation instead of reaction

Artificial intelligence analyzes historical patterns and equipment behaviors to identify variations that indicate a possible failure.

The AI can:

🔍 Detect early anomalies

Identifies subtle changes in behavior that precede failures.

⏱️ Estimate when equipment will stop functioning correctly

Predicts the remaining time before intervention is required.

⚠️ Identify risk conditions

Signals situations that increase the probability of failure.

✂️ Reduce unnecessary interventions

Avoids preventive maintenance that doesn't add real value.

📅 Recommend optimal times for maintenance

Suggests ideal time windows to intervene without affecting production.

🔗 Correlate variations with probable causes

Relates symptoms to possible causes based on historical data.

This approach can reduce downtime by up to 40%, depending on the type of operation and the state of the assets.

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Clear information for decision-making

The platform presents key data and indicators in simple and practical dashboards:

• General asset status
• Failure probability
• Anomaly severity
• Estimated time before intervention
• Operational impact
• Comparative analysis by shifts, days, or cycles
• Probable causes according to historical behavior

This allows maintenance teams to act with anticipation and precision.

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Real downtime reduction

Predictive maintenance not only reduces breakdowns but also:

✅ Improves equipment availability

✅ Decreases unexpected stops

✅ Avoids cost overruns from urgent repairs

✅ Increases machinery lifespan

✅ Optimizes resource planning

All this translates into operational continuity and greater efficiency.

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Practical example

A production line begins to show a slight increase in vibration recorded by existing equipment.

Visually it doesn't seem like a problem, but the AI detects a deviation from historical behavior.

The platform alerts that:

"The machine shows a pattern indicating possible failure within 7 to 10 days."

The maintenance team acts in time → the intervention is minimal and operation doesn't stop.

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Flexible for different asset types

The solution adapts to equipment such as:

• Motors
• Pumps
• Fans
• Compressors
• Production machinery
• Systems with thermal or mechanical variations
• Equipment subject to cyclical wear

Depending on existing infrastructure, fully cloud or hybrid configurations can be evaluated, combining local modules when necessary.

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Part of the 2025 Roadmap

The Predictive Maintenance System is part of our 2025 Roadmap, aimed at building digital tools that drive operational efficiency.

Currently developing modules for:

• Advanced analytics
• Anomaly detection
• AI predictions
• Flexible connectivity
• Critical asset visualization

Capabilities will evolve progressively throughout 2025.

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🟩 Note for companies interested in AI innovation

We are looking for companies that want to participate as early partners in adopting AI-powered predictive maintenance technologies.

If your organization is interested in:

• Anticipating failures
• Reducing downtime
• Optimizing maintenance
• Collaborating on the evolution of new capabilities

We can establish an early implementation agreement, with exclusive benefits for the first partners.

Contact us to coordinate an exploratory meeting.

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Conclusion

Predictive maintenance represents a necessary evolution: moving from reaction to anticipation.

A platform based on data and AI allows reducing downtime, avoiding costly failures, and maintaining operations at peak performance.

The operational continuity of the future is built with data, intelligence, and informed decisions.

This solution is designed to accompany companies in that direction.