Industrial AI solution

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.

Plan the use case Explore ROI tools

Predictive maintenance AI for oil and gas rotating equipment

Acceptance before scale

Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

Direct 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.

Private deployment available

Why evaluate it

Critical Rotating Equipment Failures Are Predictable.

The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

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.

Candidate capabilities

What a production solution may need to do.

Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

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

Architecture context

Select components after the boundary and test are clear.

The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

Historian & data

OSIsoft PI
Industry-standard historian
PI AF / Asset Framework
Asset model and hierarchy
Aveva PI Vision
Operator visualisation
Honeywell Experion PHD
Refinery historian

Condition monitoring

Bently Nevada 3500 / Orbit
API 670 vibration monitoring
GE System 1 / Aveva APM
Existing CM platforms
SKF IMx / @ptitude
Vibration analysis

ML & analytics

PyTorch / TensorFlow
Failure prediction models
AWS Lookout for Equipment
Managed PdM
Azure ML / Databricks
Cloud training

Workflow integration

SAP PM
Notifications and work orders
IBM Maximo
CMMS integration
ServiceNow ITOM
Operations workflows

Planning ranges

A standard path from decision to operation.

Discovery and qualification

1-2 weeks

Named workflow, owner, baseline, risks and go/no-go questions.

Readiness assessment

2-4 weeks

Data, integration, security, value and operating-readiness findings.

Technical proof of value

4-6 weeks

A bounded test on representative data with documented limitations.

Production pilot

8-12 weeks

One controlled workflow, integrated and evaluated with real users.

Enterprise or multi-site rollout

3-6+ months

Phased scale-out, monitoring, support and change management.

AI capability or CoE programme

3-6+ months

Governance, delivery methods, reusable assets and team enablement.

These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

Production safeguards

Private deployment is one control, not the whole control system.

Data boundary

Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
Document data flows, storage, deletion, backups and support access before production.
The customer approves every interface and any permitted external connection.

Model and application controls

Evaluate representative cases, uncertainty and harmful failure modes before use.
Use suitable access control, input handling and output guardrails for the selected risk.
Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

Traceability and operation

Define identity, roles, logs, monitoring, updates, backup and incident handling.
Keep the evidence needed to investigate outputs and reproduce important decisions.
Assign a named business and technical owner for production operation.

Human authority

Domain, quality, safety and regulatory owners retain decision authority.
Compliance depends on the implemented system and the customer's validated controls.
Escalation and safe fallback are part of the acceptance criteria.

Limitations to plan for

Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

Buyer questions

Direct answers before you plan a pilot.

How does this work with our existing Bently Nevada or GE System 1?+

We sit alongside, not replace. Bently Nevada / System 1 continue to handle API 670 protection and standard condition monitoring. Our ML layer consumes their waveform and trend data plus process variables from PI to provide earlier prognostics and failure-mode classification that traditional CM thresholds miss.

Can this run on offshore platforms with limited bandwidth?+

Yes. We deploy edge inference at the platform on hardened industrial PCs or NVIDIA Jetson. Only model outputs (failure probability, RUL, fault class) and compressed feature summaries are shipped to shore. Full waveforms stay on-platform unless requested for engineering review.

How do you handle different operating regimes (startup, full load, surge control)?+

Models are regime-aware. We segment historian data by operating mode using process variables and PI AF context, then train separate sub-models or use mixture-of-experts architectures so normal startup behaviour isn't flagged as an anomaly.

What about cyber security for the OT network?+

All data flows through your historian or a one-way data diode into the IT / analytics zone. Edge inference nodes are deployed inside your Purdue Level 3 with explicit firewall rules. We follow IEC 62443 zone and conduit design and work with your OT security team on architecture review.

What's a realistic pilot scope?+

This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.

What ROI have operators seen?+

There is no responsible universal ROI figure. Build the case from the selected workflow's baseline, error or downtime exposure, detectable opportunity, adoption, false-alert cost and full operating cost. NeoBram's calculators are illustrative planning tools; replace every assumption with customer evidence before an investment decision.

Bring one workflow

Define the evidence, boundary and acceptance test together.

Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

Plan the first project