🛢️ Oil & Gas · Data Flow
Compressor Predictive Maintenance
How a gas compressor station predicts failures: vibration and process data, an ML model, and maintenance work orders before a trip happens.
Drawing diagram…
What this diagram shows
- Vibration, bearing temperature and process data are combined
- The model compares live behaviour with a normal baseline
- Early warnings become planned maintenance, avoiding unplanned trips
Prompt used
Data flow for compressor predictive maintenance: vibration monitoring (Bently Nevada), bearing temperatures and process data (suction/discharge pressure, flow) from the compressor station go through the control system to the historian. An anomaly detection model compares live data with a learned normal baseline and estimates time to failure, raising early warnings to the reliability engineer, who creates a planned work order in SAP PM; outcomes are fed back to tune the model.
Mermaid code
flowchart LR VIB[Vibration monitoring] -->|Overall and spectra| DCS[Control System] TMP[Bearing temperatures] --> DCS PRC[Suction and discharge pressure, flow] --> DCS DCS --> HIS[(Historian)] HIS --> AD[Anomaly Model - normal baseline] AD -->|Deviation score, time to failure| RE[Reliability Engineer] RE -->|Planned work order| PM[SAP PM] PM -->|Findings and repairs| LBL[(Failure Records)] LBL -->|Tuning| AD
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