Predictive Maintenance Intelligence for Gas Compression Systems

The Challenge

A leading energy operator faced recurring unplanned shutdowns and inefficiencies in gas compression operations. Their monitoring system relied on manual inspection of PI data and spreadsheet-based performance tracking, which made it difficult to identify early warning signs of component degradation. Operational parameters such as turbine pressure, lube oil pressure, discharge temperature, and seal gas differentials were logged inconsistently, leading to undetected anomalies and high maintenance costs.

Our Solution

Designed an automated predictive maintenance pipeline architecture on Databricks, integrating real-time PI system data with historical compressor telemetry. The system applied predefined engineering rules for each operational stage—covering Enclosure, Pre-lube, Yard Valve, Ignition, and On-load conditions—to flag deviations from normal operating ranges. Anomaly detection models tracked sensor trends like voltage stability, seal gas pressure, and turbine differential pressure to identify early signs of failure. A machine learning layer powered by MLflow-tracked XGBoost and time-series anomaly detection models predicted potential component failures up to 7 days in advance. The workflow used Delta Live Tables for data standardization, and LangGraph orchestration to trigger automated alerts and generate daily performance summaries for engineers.

Results & Outcomes

Technologies Used