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Predictive Maintenance Analytics

Use IoT sensors and ML to predict failures, optimize maintenance timing, and reduce downtime

Complexity: Complex | Duration: 30m+ | Category: Operations/Process

Tags: #predictive-maintenance #iot #machine-learning #analytics #condition-monitoring

Workflow Diagram​

Steps​

Step 1: Sensor Deployment​

Agent: iot

specialist - Install vibration, temperature, pressure sensors on assets

Step 2: Data Collection​

Agent: data

engineer - Set up real-time data streaming to analytics platform

Step 3: Historical Analysis​

Agent: data

scientist - Analyze past failure patterns and leading indicators

Step 4: Feature Engineering​

Agent: ml

engineer - Create predictive features from sensor data

Step 5: Model Training​

Agent: ml

engineer - Train ML models (random forest, LSTM, etc.) on failure data

Step 6: Model Validation​

Agent: data

scientist - Validate accuracy, precision, recall on test set

Step 7: Threshold Configuration​

Agent: maintenance

specialist - Set alert thresholds for intervention

Step 8: Alert Integration​

Agent: iot

specialist - Connect predictions to CMMS work order creation

Step 9: Dashboard Creation​

Agent: data

analyst - Build real-time asset health monitoring dashboards

Step 10: Continuous Improvement​

Agent: data

scientist - Retrain models monthly with new failure data

Usage​

To execute this workflow:

/workflow operations/process/predictive-maintenance-analytics.workflow

See other workflows in this category for related automation patterns.