AeroPulse
AI platform for wind turbine health monitoring that combines asset condition insights with API-driven wind market intelligence to support maintenance prioritization and capital allocation decisions.
The Problem
“Wind turbine maintenance and capital allocation decisions are delayed by siloed asset health data and disconnected market intelligence”
Organizations face these key challenges:
SCADA, CMMS, inspection, and reliability data are fragmented across tools and vendors
Market intelligence is often trapped in PDFs, analyst reports, and disconnected data feeds
Maintenance prioritization is reactive and dependent on manual expert review
Capital allocation workflows are slow, spreadsheet-heavy, and difficult to standardize
Impact When Solved
The Shift
Human Does
- •Review SCADA trends, vibration logs, inspection findings, and maintenance history across separate sources
- •Manually triage turbine issues and decide which assets need near-term intervention
- •Compile wind forecasts, pricing, curtailment risk, and market reports into planning spreadsheets
- •Assess project economics and prioritize maintenance or capital allocation in review meetings
Automation
- •No consistent AI support; data aggregation and analysis are mostly manual
- •Limited rule-based alarms highlight threshold breaches without broader context
- •Static reporting tools summarize historical performance after manual preparation
Human Does
- •Approve maintenance actions, outage timing, and intervention priorities for flagged assets
- •Review AI-ranked repair, defer, or accelerate recommendations against operational constraints
- •Decide capital allocation across assets and projects based on scenario tradeoffs and business goals
AI Handles
- •Continuously monitor turbine telemetry, alarms, inspection notes, and maintenance records for anomalies
- •Estimate failure risk, remaining useful life proxies, and expected production or revenue impact
- •Fuse asset condition with market, forecast, pricing, and curtailment intelligence into ranked actions
- •Generate daily summaries, maintenance triage recommendations, and scenario-based portfolio decision support
Operating Intelligence
How AeroPulse runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve maintenance actions, outage timing, or intervention priorities without review by a reliability manager or maintenance planner [S1][S2].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AeroPulse implementations:
Key Players
Companies actively working on AeroPulse solutions:
Real-World Use Cases
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