From Reactive Repairs to Data‑Driven Uptime

Unexpected equipment failures cost the global energy sector billions in lost production, environmental penalties and safety incidents. SDUK’s PhD‑led reliability team first tackled these challenges on offshore platforms and LNG facilities—remote sites where a single compressor outage can halt an entire value chain.

We ingest high‑frequency sensor streams, historian logs and maintenance records, then combine anomaly detection, survival analysis and physics‑informed models to forecast degradation days or even weeks in advance.

Common Pitfalls We Eliminate

• Siloed Data
  Disparate OT and IT repositories hide causal patterns. We unify sources in real time, preserving lineage so root‑cause signals remain intact.
• Over‑Fitted Models
  Algorithms trained only on common events overlook rare, catastrophic failures. Our ensemble approach balances precision and recall, retaining sensitivity to low‑frequency—but high‑impact—anomalies.
• Alert Overload
  Generic thresholds spam technicians with noise. SDUK embeds context‑aware prioritisation and prescriptive guidance, turning raw alerts into clear work orders that schedule parts, personnel and optimal shutdown windows automatically.

The Outcome

Fewer unplanned shutdowns, optimised spares inventory and a culture of proactive reliability that translates seamlessly from oil & gas to manufacturing, utilities, transportation and beyond.