AI Workflow for Automated Corrosion Under Insulation (CUI) Prediction

The primary business impact is preventing catastrophic, unplanned asset failures. By modeling CUI probability from thermal imaging, insulation condition, and pipe service history, the workflow identifies high-risk sections for proactive inspection and repair. This directly avoids the multi-million dollar costs of emergency shutdowns, environmental remediation, regulatory fines, and production loss associated with a leak.

Manual, blanket inspection campaigns are labor-intensive and inefficient. This workflow automates inspection prioritization, directing crews and resources only to pipes with the highest predicted risk. This shifts capital from broad, time-based programs to targeted, condition-based interventions, dramatically improving the ROI of your integrity management budget and extending inspection cycles for low-risk assets.

The workflow automates the manual correlation of disparate data sources (thermal, historical, process) that engineers typically spend weeks analyzing. By generating pre-populated work packages with risk scores, location, and recommended actions, it cuts planning time and ensures field crews are more effective. This reduces both internal labor overhead and external contractor hours spent on exploratory insulation removal.

Proactive CUI management directly extends the service life of high-value piping assets. By catching corrosion early during planned turnarounds, repairs are less extensive and more cost-effective. This workflow provides the data-driven justification to defer full pipe segment replacements, protecting capital budgets and improving the net present value (NPV) of your asset portfolio.

For regulated industries, demonstrating a proactive, data-driven integrity management program is critical. This workflow creates a defensible, auditable trail from risk prediction to inspection to repair. Automated reporting and integration with systems like API 570-compliant PIMS (Pipeline Integrity Management Systems) or plant CMMS (IBM Maximo, SAP PM) ensure consistent compliance and reduce audit preparation burden.

Major plant turnarounds are high-stakes, schedule-critical events. This workflow feeds prioritized, scoped work lists directly into turnaround planning tools months in advance. This allows for accurate parts ordering, crew scheduling, and scope locking, preventing last-minute discoveries that blow budgets and delay startup. The result is more predictable, lower-cost turnarounds.

The workflow ingests and aligns disparate data streams critical for CUI modeling. This includes thermal imaging (to detect moisture anomalies), insulation condition logs from CMMS (like SAP or IBM Maximo), pipe service history (temperature, chemistry), and environmental exposure data. An orchestration agent uses spatial and temporal alignment to create a unified asset health record, overcoming data silos that delay manual analysis.

A dedicated AI agent applies a physics-informed machine learning model to the fused data. It calculates a time-to-failure probability for each insulated pipe segment by modeling the corrosion rate based on moisture presence, insulation breakdown, and service conditions. The agent outputs a prioritized risk score, automatically flagging segments requiring immediate inspection versus those for scheduled monitoring.

For high-risk segments, the system automatically generates detailed work packages. Using LangGraph or a similar orchestrator, it retrieves standard operating procedures (SOPs) for insulation removal, specifies required NDT methods (ultrasonic thickness testing), pulls material codes from the ERP, and estimates labor hours. These packages are routed directly to the CMMS as pre-populated work orders, eliminating manual scoping and reducing turnaround from detection to action.

The core architecture is built for enterprise integration. Risk scores and inspection findings are written back to the Pipeline Integrity Management System (PIMS) or Asset Performance Management (APM) platform to update the corrosion management plan. Work orders and findings sync bi-directionally with the CMMS (e.g., SAP PM, Oracle EAM). This closes the loop, ensuring the digital twin of plant integrity is always current and audit-ready.

To maintain engineering governance, the workflow embeds mandatory approval checkpoints. High-cost work orders (e.g., full insulation replacement) are routed to a corrosion engineer for review and budget approval before release. Low-confidence AI predictions are flagged for a rapid human validation step using a dashboard that presents the agent's rationale and supporting imagery. This ensures safety and control while automating routine decisions.

The system includes a monitoring layer that tracks model performance drift, inspection outcome validation (comparing prediction to actual findings), and process latency. Failed predictions are routed to a feedback loop to retrain the risk model. Dashboards provide ops teams with metrics on predicted vs. actual leaks prevented, ROI on inspection campaigns, and backlog aging.