Predictive Maintenance & IoT Analytics
Prevent equipment failures before they happen. We build ML-driven predictive maintenance platforms that analyse IoT sensor data to forecast equipment degradation, optimise maintenance schedules, and reduce unplanned downtime across your operations.
From Reactive Repairs to Predictive Intelligence
Unplanned equipment downtime is one of the most expensive operational risks in manufacturing and maritime operations. Traditional time-based maintenance either replaces components too early — wasting useful life — or too late, after a failure has already disrupted production. Predictive maintenance uses machine learning to find the optimal intervention point based on actual equipment condition.
skios builds predictive maintenance platforms that ingest data from IoT sensors — vibration, temperature, pressure, current draw, acoustic signatures — and apply ML models trained on your equipment's historical failure patterns. The platform identifies degradation trends, predicts remaining useful life, and generates maintenance recommendations that your operations team can act on before failures occur. Integration with your CMMS or ERP ensures maintenance orders are created automatically when intervention thresholds are reached.
Predictive Maintenance Capabilities
IoT Sensor Integration
Ingest data from vibration, temperature, pressure, and acoustic sensors via MQTT, OPC-UA, or REST endpoints.
ML Failure Prediction
Machine learning models trained on your equipment data to predict failures and estimate remaining useful life.
Maintenance Optimisation
Condition-based maintenance scheduling that maximises equipment uptime while minimising unnecessary interventions.
Operations Dashboard
Real-time equipment health monitoring with alert thresholds, trend visualisation, and maintenance recommendation tracking.