Ingest machine data
Vibration, temperature, throughput, and maintenance history are aligned to asset hierarchy.
/ Operações e Automação /
Conserte antes que quebre. Depois não.
IA que monitoriza o estado do equipamento, deteta sinais de falha e recomenda ações de manutenção — para que o tempo de inatividade não planeado se torne uma exceção gerida e não uma crise recorrente.
The problem
Unplanned downtime derails operations without warning
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Unplanned downtime that derails operations
Equipment failures happen without warning. The cost isn't just repair — it's lost production, missed SLAs and emergency contractor rates.
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Scheduled maintenance that wastes resource
Fixed-interval maintenance replaces parts that are still functional and misses components that are actually degrading.
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Sensor data collected but never acted on
Equipment generates enormous volumes of operational data. Without AI to interpret it, that data sits unused while failures develop.
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Plans disconnected from operations
Maintenance schedules ignore production priorities and asset criticality. Downtime shifts rather than shrinks.
Como funciona
From sensor noise to work orders—with fewer false alarms
Forecast failure windows
Models estimate risk horizons and recommended actions—parts, crew, and downtime trade-offs included.
Close with CMMS
Recommended work orders are created or updated; feedback loops improve predictions per asset class.
Edge deployments supported when plants cannot rely on constant cloud connectivity.
Conserte antes que quebre. Depois não.
O que inclui
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
Sensor data integration
Connects to IoT sensors, SCADA systems and operational data sources across your asset base.
Failure prediction models
Detects early signs of component degradation before failure occurs.
Maintenance recommendation engine
Suggests the right maintenance action, at the right time, for the right asset.
Asset health dashboard
Real-time view of equipment status, risk scores and upcoming maintenance priorities.
Work order automation
Triggers maintenance work orders in your CMMS when thresholds are crossed.
Failure pattern library
Builds an organisation-specific library of failure signatures that improves prediction accuracy over time.
Com tecnologia de Thinkia Synapse
Resultados
What changes when this runs in production
Results vary by asset type, sensor coverage and operational environment.
–40%
Reduction in unplanned equipment failures after deployment
–25%
Reduction from eliminating unnecessary scheduled interventions
+30%
Improvement in asset reliability with condition-based maintenance
Como trabalhamos
From calendar maintenance to signals that prevent unplanned stops
Asset criticality
Asset criticality
Week 1–2
Lines, sensors, and failure modes are ranked by downtime cost and data availability.
Model & alert
Model & alert
Week 3–5
Thresholds, lead times, and work-order integration are tuned with maintenance planners.
Field pilot
Field pilot
Week 6–9
Technicians validate alerts; false positives are reduced before broadening asset classes.
Fleet scale
Fleet scale
Week 10+
Spares, crews, and schedules align to predicted demand; continuous learning respects safety gates.
Sensor coverage and industrial network policies set pace; we start where telemetry is trustworthy.
Ir mais fundo
Reliability, prediction, and proactive AI
Começar
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.