Overview
LNG plant maintenance requires more than simple document retrieval. It is a domain that demands reasoning based on diagrams, equipment relationships, operating conditions, and records of past responses.
For industrial systems with this kind of structure, Revna provides maintenance AI that combines P&ID graph conversion, data integration, and expert reasoning traces (ERT).
Outcomes
- Reduced root cause analysis (RCA) and recovery time
- Consistent troubleshooting across shifts and sites
- Less dependence on individual expertise and lower knowledge-transfer risk
- Strong traceability through recorded reasoning and verification history
What We Deliver
Digital Workbench
We represent P&IDs as interactive graphs and integrate sensors, alerts, process historians, and related documents into a unified view. The system also records interactions between operators and AI, as well as verification actions, making the maintenance process visible and traceable.
Structured Reasoning Data
We capture and structure how experts form hypotheses, what they check, and how they make decisions. By recording who checked what, when, and why, the system supports continuous improvement over time.
Custom Reasoning Models
We optimize models for each customer's specific diagrams, equipment configurations, documents, and operational data. These models are designed for real-world use, including multi-step reasoning, external tool use, and self-verification.
Hosting and Operations
We provide end-to-end support, from secure hosting to performance monitoring and continuous model updates.
Implementation Process
1. Requirements Definition and Design
We identify high-priority failure modes, target equipment, and relevant data, and design the reasoning workflow.
2. Workbench Build and Integration
We digitize P&IDs and equipment information, then connect them to operational data, logs, and document systems.
3. Data Capture
We record expert handling of real and simulated cases, building a history of reasoning steps and tool usage.
4. Training
We train models on expert reasoning histories so they can recommend the next checks and actions to take.
5. Deployment and Improvement
We track KPIs through real-world use and continuously incorporate new data into the system.
Use Cases
- Diagnosing the causes of compressor trips
- Analyzing flow anomalies and temperature or pressure drift
- Diagnosing sensor mismatches and instrumentation issues
- Recommending preventive maintenance actions with supporting evidence