Predictive Maintenance for Midstream Energy
Shifting from reactive, schedule-based maintenance to condition-based intelligence for high-value energy assets.
The Challenge
The operator relied on fixed, time-based maintenance schedules that ignored the actual condition of compressors and pipelines. This led to surprise failures, emergency repairs, and escalating operational costs.
What We Did
FutureStrive integrated sensor feeds, SCADA data, and historical records into a centralized analytical environment. We deployed cloud-based machine learning models:
- Gradient-boosted classifiers to assess immediate failure risk.
- Regression models to estimate remaining useful life (RUL).
The Predictive Maintenance Lifecycle
Effective predictive maintenance relies on a continuous loop of data ingestion, feature engineering, and model inference to detect degradation before it impacts performance.