PREDICTIVE MAINTENANCE MONITORING WORKFLOW

Created by ChecklistGuro (https://checklistguro.com)

[ ] Fetch Sensor Telemetry: Retrieve the latest vibration, temperature, and pressure readings from the IoT sensor data model.
[ ] Get Asset Metadata: Retrieve machine specifications, maintenance history, and threshold limits from the Equipment data model.
[ ] Calculate Health Score: Execute a formula comparing current sensor values against predefined threshold limits to determine the degradation percentage.
[ ] Calculate Remaining Useful Life (RUL): Calculate the estimated time until failure based on the rate of change in sensor degradation.
[ ] Average Weekly Vibration: Aggregate the last 7 days of vibration data to identify upward trends in mechanical oscillation.
[ ] Update Asset Health Status: Update the 'Current Condition' field in the Equipment data model based on the calculated health score.
[ ] Create Anomaly Alert: Create a new entry in the Alerts data model when sensor values exceed safety thresholds.
[ ] Assign Maintenance Inspection: Create a task for the Maintenance Technician to perform a physical inspection of the flagged asset.
[ ] Determine Priority Level: Calculate task priority (Low, Medium, High, Critical) based on the RUL and the impact of asset failure.
[ ] Notify Maintenance Manager: Send an email notification to the Maintenance Manager containing the anomaly details and the calculated risk level.
[ ] Generate Work Order: Create a specific task for the Parts Department to check availability of replacement components.
[ ] Fetch Inventory Levels: Retrieve stock levels for critical spare parts from the Inventory data model.
[ ] Log Maintenance Event: Update the Maintenance Log entry to record that a predictive alert was triggered and an inspection was initiated.
[ ] Generate Monthly Health Report: Create a summary report aggregating all detected anomalies and completed inspections for the monthly management review.
[ ] Emergency Alert SMS: Send an urgent SMS to the On-Call Engineer if the health score drops below the critical threshold (e.g., < 10%).

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