Predictive equipment maintenance scheduling

Step-by-Step Breakdown

Detailed explanation of each automated stage in the workflow.

1. 1

   Trigger

   Detect equipment anomaly

   SCADA system identifies abnormal sensor readings from transformers, generators, or transmission equipment. Real-time monitoring flags deviations from normal operating parameters.

   SCADA systemsOSIsoft PI historian
2. 2

   Action

   Analyze historical performance data

   PI historian retrieves equipment performance trends and failure patterns from the last 2-5 years. Data includes temperature, vibration, oil quality, and load metrics.

   OSIsoft PI historian
3. 3

   Action

   Calculate failure probability

   AI algorithms process current anomaly data against historical patterns to predict failure probability and estimated time to failure. Machine learning models assess multiple equipment parameters simultaneously.

   OSIsoft PI historian
4. 4

   Decision

   Evaluate maintenance urgency

   System determines if predicted failure requires immediate attention (within 7 days), scheduled maintenance (within 30 days), or continued monitoring based on criticality scores.

   Maximo asset management
5. 5

   Action

   Generate maintenance work order

   Maximo creates work orders with specific maintenance tasks, required parts, and skilled technicians. System checks parts inventory and technician availability automatically.

   Maximo asset management
6. 6

   Action

   Optimize maintenance schedule

   PowerWorld simulation validates that planned equipment downtime won't compromise grid stability. System reschedules if necessary to maintain reliable power delivery.

   PowerWorld simulationMaximo asset management
7. 7

   Output

   Deploy maintenance crew

   Finalized work orders are dispatched to field crews with equipment locations from GIS mapping. Technicians receive detailed maintenance instructions and safety protocols.

   Maximo asset managementGIS mapping software

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