AI-Driven Workflow for Autonomous Modeling of Creep in Power Plant Components

Manual creep lifing analysis for aging power plant assets is a bottleneck, delaying critical maintenance and life-extension decisions. This custom workflow automates the ingestion of operational data (temperature, pressure, hours), material databases (NIMS, ASME), and proprietary lifing models to run continuous, predictive simulations. The business value is direct: it converts idle asset data into a proactive maintenance schedule, preventing unplanned outages that cost millions per day and enabling informed capital planning for component replacement or upgrade.

Implementation integrates with plant data historians (OSIsoft PI, Wonderware), material databases, and FEA solvers (ANSYS, Abaqus) via API orchestration. The core is a LangGraph or custom Python orchestrator managing data validation, simulation job submission to HPC, and post-processing. Critical controls include confidence scoring for predictions, automated exception routing to engineering dashboards for review, and immutable audit logs for compliance. This creates a closed-loop system where telemetry continuously updates digital twins, triggering maintenance actions before failure.

This workflow automates the historically manual and expert-intensive process of setting up, running, and interpreting long-term creep simulations for superheater tubes and turbine casings. It eliminates weeks of repetitive setup per component by orchestrating data retrieval from sources like NIMS MatNavi or in-house material databases, automatically generating solver input files for ANSYS or Abaqus, and managing HPC job submission via Slurm. The operational upside is direct: engineers shift from manual configuration to reviewing high-confidence life predictions, accelerating asset health assessments from months to days and enabling proactive maintenance planning for aging thermal plants.

Implementation requires a LangGraph or similar orchestration layer to manage the agentic state machine, with strict controls at each stage. The Material Data Agent validates and normalizes retrieved properties, logging any data quality issues. The Convergence Monitor uses ML to detect solver instability, triggering automated parameter adjustments within predefined guardrails. All agent decisions and simulation parameters are logged to an immutable audit trail for engineering review. The final life prediction report is routed through a mandatory approval gate in a system like ServiceNow or SAP PM before any maintenance work orders are generated, ensuring governance over critical asset decisions.

Phase 1 establishes a foundational orchestration layer using LangGraph, connecting to a curated subset of material property data (e.g., NIMS Creep Data Sheets) and a single, validated lifing model. This initial workflow automates batch simulation of a defined component set, generating rupture life predictions with a mandatory human-in-the-loop review gate. This controlled scope delivers rapid ROI by eliminating manual data collation and simulation setup for routine assessments, while building trust in the automated pipeline's outputs before broader integration.

Phase 2 integrates the workflow with enterprise PLM systems (e.g., Siemens Teamcenter) and additional data sources, such as in-service inspection logs. Concurrently, a comprehensive observability layer is deployed, logging all agent decisions, data lineage, and model confidence scores for auditability. Phase 3 introduces advanced autonomy, including multi-model consensus validation and conditional auto-approval for predictions within pre-defined confidence bounds, fully realizing the workflow's potential to accelerate life-assessment studies and inform predictive maintenance schedules across the fleet.

Operationalizing autonomous creep modeling demands a control plane that enforces data quality, validates simulation convergence, and routes high-risk predictions for human review. The architecture integrates with material databases (e.g., NIMS Creep Data Sheets) and legacy asset management systems like SAP Plant Maintenance or GE APM. Governance is enforced through automated checks on input data distributions, confidence scoring on model outputs, and mandatory review gates for components nearing predicted end-of-life, ensuring engineering oversight before any maintenance work order is generated.

A phased rollout mitigates risk. Phase 1 targets non-critical auxiliary piping to validate the workflow's accuracy and build operator trust, with all outputs routed to a shadow dashboard alongside manual calculations. Phase 2 extends to secondary superheater sections, enabling semi-automated advisories with mandatory supervisor sign-off. The final phase deploys to primary high-energy piping and turbine casings, integrating fully with the CMMS for closed-loop work order generation. Each phase includes a retrospective to calibrate confidence thresholds and exception handling, ensuring the system scales without compromising asset integrity or regulatory compliance.