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Building a Predictive Maintenance System with IoT Sensor Data

Learn how to analyze IoT data collected from equipment to predict failures in advance and increase uptime through planned maintenance.

POLYGLOTSOFT Tech Team2026-01-059 min read0
IoTPredictive MaintenanceOPC-UAMQTT

What Is Predictive Maintenance?

Predictive Maintenance is a strategy that analyzes equipment data to predict failures and performs maintenance before breakdowns occur.

The Evolution of Maintenance Strategies

  • Reactive Maintenance: Repair after breakdown
  • Preventive Maintenance: Scheduled inspections and replacements
  • Predictive Maintenance: Data-driven failure prediction
  • IoT Data Collection

    Sensor Types

  • Vibration sensors: Motor and bearing condition monitoring
  • Temperature sensors: Overheating detection
  • Current sensors: Power anomaly detection
  • Pressure sensors: Hydraulic/pneumatic system monitoring
  • Communication Protocols

  • OPC-UA: Industrial standard protocol
  • MQTT: Lightweight IoT protocol
  • Modbus: Legacy equipment integration
  • Building Prediction Models

    Data Preprocessing

    Perform noise removal, missing value handling, and feature engineering.

    Model Training

    Use time-series analysis and anomaly detection algorithms to learn failure patterns.

    Alarms and Actions

    When a failure is predicted, send alerts to responsible personnel and schedule maintenance work.

    Expected Benefits

    When implementing a predictive maintenance system:

  • Unplanned downtime reduced by 70%
  • Maintenance costs reduced by 25%
  • Equipment lifespan extended by 20%
  • OEE improved by 15%
  • Conclusion

    Predictive maintenance combining IoT and AI technologies is a core element of smart factories. Build your predictive maintenance system with POLYGLOTSOFT's IoT Gateway and AI platform.

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