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Predictive Maintenance
Predictive maintenance uses IoT sensor data to forecast when equipment might fail, allowing for maintenance to be performed just before it’s needed.
Core components:
Sensor Integration - Collecting real-time data on equipment conditions (vibration, temperature, sound, etc.)
Data Processing - Cleaning, normalizing, and preparing sensor data for analysis
Condition Monitoring - Tracking equipment state against established parameters
Failure Prediction Models - Using machine learning to forecast potential failures based on historical patterns
Implementation Architecture
In the IoT, these capabilities typically include:
Edge Processing - Initial anomaly detection at the device level to reduce latency.
Fog Computing - Intermediate processing for time-sensitive analytics.
Cloud Backend - Advanced analytics, model training, and long-term data storage.
Visualization Layer - Dashboards and alerts for maintenance teams.
Benefits
Reduced Downtime - Preventing unexpected equipment failures.
Cost Optimization - Performing maintenance only when necessary.
Extended Asset Lifespan - Addressing issues before they cause permanent damage.
Improved Safety - Preventing catastrophic failures that could pose safety risks.