Predictive Maintenance AI
For Oil & Gas Operations
Predict failures on compressors, pumps, turbines and critical rotating equipment across upstream, midstream and downstream. Integrated with PI Historian, SAP PM and Maximo.
Quick Answer
NeoBram delivers predictive maintenance AI for oil and gas operators - failure prediction for centrifugal compressors, multistage pumps and gas turbines using PI Historian, vibration and process data, integrated with SAP PM and Maximo, with edge inference at the asset for latency-critical alarms.
Why This Matters
Critical Rotating Equipment Failures Are Predictable.
A single compressor trip on an LNG train or offshore platform can cost millions per day in deferred production. Reciprocating-compressor reliability is still the #1 maintenance issue across upstream and midstream operators worldwide.
Modern PdM combines high-frequency vibration, process variables (suction/discharge pressure, temperature, flow) and historian context with ML models trained on your asset population. It detects fouling, seal degradation, bearing wear and surge precursors weeks before traditional condition monitoring.
We deploy production PdM aligned with API 670 / 678, on your existing PI / OSIsoft stack, with bidirectional integration to SAP PM or Maximo. Edge inference at the asset keeps raw data on-platform and meets latency requirements for safety-critical alarms.
Our Tech Stack
Production-Grade Tools We Deploy
Historian & data
Condition monitoring
ML & analytics
Workflow integration
Architecture Deep-Dive
How We Build It
Rotating-equipment failure prediction
ML models trained on historian and vibration data detect bearing wear, seal degradation, fouling and misalignment on compressors, pumps and turbines.
- Centrifugal and reciprocating compressor failure modes
- Multistage pump cavitation and seal degradation
- Gas-turbine hot-section and bearing prognostics
- Surge and stall precursor detection
Safety-critical early warning
Sub-second edge inference at the asset for safety-critical conditions - surge, overspeed, seal failure - that need to alarm before historian compression delays the signal.
- Edge inference on Jetson or industrial PC at the asset
- Bypass of historian compression for latency-critical signals
- Integration with safety instrumented system (SIS) alarms
- Operator HMI overlay in PI Vision
Fleet learning across assets
Train models on your full fleet of similar assets (e.g. 40 centrifugal compressors across 6 fields) so a failure mode seen on one asset is detected earlier on the next.
- Multi-asset transfer learning across similar equipment
- Federated learning across operating regions
- Per-asset fine-tuning for site-specific operating conditions
- Continuous retraining as new failure modes are labelled
CMMS workflow integration
Bidirectional integration with SAP PM and Maximo so predicted failures become notifications and work orders with recommended parts, procedures and isolation requirements.
- Auto-create SAP PM notifications with failure mode
- Maximo work-order generation with BOM and tools
- Permit-to-work and isolation linking
- Outcome feedback loop into model retraining
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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