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Predictive Maintenance Solutions

Cut unplanned downtime with sensors, edge and analytics. Iotplace connects industrial sites and IIoT startups.

Machine sensors, alerts, maintenance before failure.

Predictive maintenance uses sensors (vibration, temperature, current, acoustic), edge computing and ML models to anticipate failures on rotating machinery, production lines and critical infrastructure — before costly breakdowns.

Market & drivers

Potential ⭐⭐⭐⭐⭐. Industry 4.0, hourly downtime cost and field maintenance shortages accelerate adoption. Typical projects: pilot 10–50 machines then site scale.

Typical use cases

Vibration monitoring

Bearings, pumps, compressors — thresholds and FFT spectra.

Edge + cloud analytics

Local inference, cloud aggregation, OEE dashboards.

CMMS integration

Automatic work orders on alert.

Light digital twins

Asset model + sensor history for simulation.

Project challenges

  • Sensor data quality and volume
  • False positives / field adoption
  • ATEX, dust, EMI environments
  • Maintenance + IT alignment

How Iotplace helps

For startups

  • Access maintenance missions on industrial sites
  • Paid PoC application to validate client seriousness
  • Mission payment after algorithms + platform delivery
Apply in Predictive Maintenance Solutions

Predictive Maintenance Solutions — FAQ

How to launch Predictive Maintenance?

Condition Monitoring PoC on 10–50 machines: vibration sensors, edge AI, SCADA/OPC-UA. Publish scope on Iotplace.

Should we start with a PoC?

Recommended: validate sensors, connectivity and models on a limited line before multi-site scale.

Common stacks on Iotplace?

MQTT, OPC-UA, TimescaleDB, Influx, AWS IoT, Azure IoT, embedded TensorFlow Lite, Grafana/custom dashboards.