Nuclear Inspection Campaign Planning Digital Worker
Orchestrates nine specialized AI agents that work in parallel to ingest historical data and real-time IoT sensors, run digital twin simulations, perform risk-based prioritization with anomaly detection, predict equipment failures, validate compliance, optimize resource allocation, and generate alternative campaign scenarios with What-If analysis. Reduces planning time from weeks to minutes through predictive maintenance integration.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Campaign Orchestrator Agent
Coordinating nine specialized AI agents across multiple analysis phases while maintaining workflow integrity and handling inter-agent dependencies.
Core Logic
Manages the complete campaign planning workflow through nine phases: data ingestion, digital twin simulation, risk and anomaly detection, predictive maintenance, compliance validation, resource optimization, schedule generation, human approval, and output generation. Coordinates parallel agent execution, handles inter-agent messaging, resolves conflicts, and synthesizes results into executive recommendations. Presents consolidated findings for human approval gates.
Data Analyst Agent
Nuclear plants have millions of historical inspection records across disparate systems. Extracting actionable insights requires significant data engineering effort.
Core Logic
Queries and integrates historical inspection databases, synchronizes real-time IoT sensor streams (temperature, pressure, vibration, radiation, thickness), performs pattern recognition and trend forecasting, and prepares unified datasets for downstream agents. Reports data quality scores, component distribution, and preliminary anomalies for the orchestrator.
Digital Twin Analyst Agent
Understanding component degradation trajectories and remaining useful life requires complex physics-based and ML hybrid models that are difficult to deploy at scale.
Core Logic
Runs digital twin simulations using Weibull survival analysis combined with neural network ensembles. Models degradation for all components with 90-day prediction horizons, calculates remaining useful life estimates, identifies accelerated degradation patterns, and generates critical alerts for components requiring immediate attention. Achieves 94%+ simulation confidence.
Risk Assessment Agent
Risk-based inspection prioritization requires analyzing multiple factors (thickness loss, age, radiation exposure, defect history, sensor anomalies) which is complex and time-consuming.
Core Logic
Calculates component risk scores using an enhanced 9-factor logistic regression model incorporating digital twin outputs and real-time sensor anomalies. Scores all components (typically 850+), identifies high/medium/low risk distributions, and produces prioritized inspection recommendations. Integrates anomaly correlation data to adjust baseline risk assessments.
Anomaly Detection Agent
Detecting anomalies across thousands of IoT sensor streams in real-time requires sophisticated multi-scale detection algorithms that can distinguish true anomalies from noise.
Core Logic
Analyzes 1,000+ active sensor streams using isolation forest and LSTM autoencoder ensemble models. Detects sensor drift, sudden changes, pattern breaks, threshold breaches, and equipment degradation signatures. Classifies anomalies by severity (low/medium/high/critical), estimates false positive rates, and correlates findings with risk assessor outputs.
Predictive Maintenance Agent
Reactive maintenance leads to unplanned outages costing millions. Predicting component failures and optimizing maintenance actions requires sophisticated ML models and cost-benefit analysis.
Core Logic
Runs failure prediction using Weibull, Random Forest, and LSTM model ensembles with 365-day prediction horizons. Calculates failure probabilities, estimates cost of inaction (typically $10M+ exposure), and generates optimal maintenance action plans (preventive replacements, scheduled inspections, enhanced monitoring). Produces ROI projections for recommended investments.
Compliance Validation Agent
Ensuring inspection campaigns meet all regulatory requirements (ASME, NRC, IAEA) while integrating risk-based additions is complex and error-prone.
Core Logic
Validates all components against ASME Section XI (IWB-2500, IWB-2600, IWB-2700, IWC-2500), NRC 10 CFR 50.55a, and IAEA NS-G-2.6 standards. Identifies mandatory inspection items per code category, adds risk-based inspections beyond minimum requirements, confirms zero compliance gaps, and generates regulatory documentation.
Resource Optimization Agent
Optimizing inspection campaign resources (personnel, robots, equipment) under multiple constraints (budget, radiation dose, time) while meeting coverage requirements is a complex multi-objective optimization problem.
Core Logic
Runs multi-objective optimization using constraint satisfaction algorithms with 10,000+ scenario evaluations. Optimizes for cost, duration, risk coverage, and resource utilization simultaneously. Identifies Pareto-optimal solutions, calculates ROI for resource additions (e.g., renting additional UT robots), and resolves bottlenecks through personnel and equipment allocation recommendations.
Schedule Generation Agent
Creating detailed inspection schedules with critical path analysis, task dependencies, and contingency buffers requires specialized project management expertise and iterative refinement.
Core Logic
Generates detailed schedules using Critical Path Method (CPM) for 300+ tasks with dependencies. Calculates float days, identifies critical path bottlenecks, runs What-If scenario simulations (equipment delays, budget changes, staff shortages), and produces multiple schedule options (baseline, accelerated, maximum coverage). Compares results against industry benchmarks.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization