Agentic Workflow for Wind Turbine Gearbox and Blade Health Monitoring

This orchestration agent ingests and time-synchronizes high-frequency SCADA data (power output, temperatures), vibration spectra from CMS, and high-resolution drone imagery of blades. It normalizes signals, handles missing data, and creates a unified feature vector for downstream diagnostics, ensuring a 360-degree view of asset health from disparate, often legacy, industrial systems.

A reasoning agent applies ensemble models (e.g., isolation forests for anomalies, CNNs on vibration spectrograms, vision models on blade imagery) to the fused data. It cross-references detected patterns against a historical fault library using vector similarity search, outputs a diagnosed fault (e.g., 'outer race bearing spall', 'leading edge erosion'), and assigns a severity score (1-5) and a confidence interval. This replaces manual engineering analysis.

This planning agent consumes fault diagnoses, Remaining Useful Life (RUL) forecasts, weather windows, crane availability, and technician certifications. It generates optimized maintenance schedules that batch interventions for multiple turbines in a farm to minimize crane mobilization costs. It automatically reserves parts from inventory systems (e.g., SAP) and creates pre-populated work orders in the CMMS (e.g., Maximo), shifting from calendar-based to condition-based planning.

A control agent manages workflow governance. For high-severity alerts or major interventions, it routes actionable summaries with supporting evidence (trend graphs, annotated images) to site managers or engineers via Slack, Teams, or a dedicated portal for review and approval. It enforces a four-eyes principle on critical actions (e.g., turbine shutdown) while allowing low-risk, routine tasks to proceed autonomously, balancing automation with operational safety.

A closing-the-loop agent monitors post-repair telemetry to validate that the intervention restored baseline performance. It automatically updates the digital twin and tags the event (fault, fix, outcome) in the historical fault library. Based on performance drift or new failure modes, it triggers the retraining of diagnostic models, creating a self-improving system that adapts to changing turbine conditions and improves prediction accuracy over time.

This is not just a dashboard but an observability agent that surfaces the health of the workflow itself—agent status, data pipeline latency, model confidence scores. It also provides a business-facing view: a real-time map of turbine health, forecasted downtime costs, and a running tally of Revenue at Risk and Costs Avoided by proactive interventions. This directly attributes the workflow's value to energy-based availability payments and O&M budget savings.

This team owns the technical specifications for fault signatures (e.g., bearing spalling, blade delamination) and validates the diagnostic logic against historical failure data. They define the thresholds for Remaining Useful Life (RUL) models and approve the integration of new data streams (e.g., SCADA vibration, drone imagery analytics) into the agentic reasoning layer. Their sign-off is required before any automated work order is generated.

Field managers define the operational constraints that shape the workflow's output: acceptable weather windows for crane operations, technician certifications, and parts availability at regional depots. The multi-agent system must route prioritized alerts into their CMMS (e.g., SAP PM, Maximo) with all contextual data (RUL, required parts, SOPs) to enable single-visit repairs, which is critical for optimizing costly mobilization.

This team architects the data pipeline from edge devices (turbine PLCs, drones) to the cloud, ensuring low-latency ingestion of time-series data into a historian (e.g., OSIsoft PI) and image data into object storage. They implement the API orchestration layer (using frameworks like LangGraph) that connects diagnostic agents to the CMMS, ERP for parts, and GIS for location data, while enforcing security and data governance policies.

For wind farms operating on availability-based contracts, this stakeholder quantifies the financial impact of downtime. They help define the cost-avoidance logic that prioritizes alerts impacting high-revenue turbines or periods of peak pricing. The workflow must produce audit-ready reports linking predictive actions to protected MWh and revenue, which is essential for securing ongoing investment in the automation platform.

This role manages the phased rollout, starting with a pilot on 5-10 turbines to validate agent accuracy and field procedures. They design the human-in-the-loop approval gates for the initial phase (e.g., all generated work orders require supervisor review) and plan the training for technicians on interpreting AI-generated instructions. Their goal is to build operational trust before moving to fully autonomous ticket creation and routing.

Critical for accessing proprietary turbine fault libraries and securing API connections to OEM remote monitoring portals (e.g., GE WindSCADA, Siemens SINAVAL). This stakeholder negotiates data-sharing agreements and ensures the custom workflow complements—rather than conflicts with—existing warranty and service contracts. They also coordinate with drone inspection or blade repair specialty contractors integrated into the automated dispatch logic.