P-101 causal attribution
Anomaly ANM-4032 · confidence-weighted evidence
Root cause confidence
89%
Explainable industrial AI
Root Cause Analysis
Move from an alarm to a defensible maintenance decision with evidence from the digital twin, signal history, and asset context.
Closed-loop AI workflow
Sensor Data
Well Digital Twin
AI Detection
4Root Cause
5Failure Prediction
6CSS + SRP Simulation
7Recommendation
Most likely failure mechanism
Drive-end bearing degradation
The digital twin identifies a bearing wear pattern that explains the vibration harmonic increase, heat rise, and progressive hydraulic efficiency loss.
Vibration increased 204% above the 14-day baseline
Bearing temperature rose 18.6 °C despite stable ambient conditions
Flow slipped 4.3% at a comparable pressure demand
Causal chain
Bearing wear → misalignment load → vibration growth → performance loss.
Secondary contributor
Lubricant contamination may be accelerating wear. Confirm through oil analysis.
Actionable conclusion
Align coupling and inspect drive-end bearing before the next high-load shift.