Turbine Performance Pulse
AI-driven predictive maintenance for wind turbines and adjacent renewable generation assets, helping operators reduce unplanned downtime, improve availability, and protect revenue through earlier fault detection and maintenance planning.
The Problem
“Reduce unplanned downtime in waste-to-energy and renewable generation assets with AI-driven predictive maintenance”
Organizations face these key challenges:
Revenue loss when waste-to-energy plants are offline unexpectedly
Difficulty meeting annual availability targets promised in RFQs
Reactive maintenance driven by alarms after degradation is already severe
Fragmented data across SCADA, historians, CMMS, and OEM systems
Impact When Solved
The Shift
Human Does
- •Review SCADA, historian, and alarm trends to spot equipment issues
- •Plan maintenance from fixed schedules, inspections, and technician judgment
- •Troubleshoot trips or performance loss after alarms or outages occur
- •Prioritize work orders and outage windows based on plant availability needs
Automation
- •Apply fixed OEM alarm thresholds to flag abnormal conditions
- •Generate basic trend charts and historical condition views
- •Log alarms and operating events for operator review
Human Does
- •Approve maintenance timing and outage plans based on AI risk insights
- •Decide how to prioritize assets and work orders against availability targets
- •Review AI-flagged exceptions and confirm corrective actions
AI Handles
- •Continuously monitor telemetry, alarms, and maintenance history for degradation patterns
- •Score asset health and predict failure risk and likely intervention windows
- •Rank assets by operational and revenue impact to focus maintenance effort
- •Suggest probable failure modes and recommended maintenance timing
Operating Intelligence
How it works
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
TurbinePulse must not approve maintenance timing or outage plans without a maintenance planner or plant operations lead making the final decision. [S1]
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 Turbine Performance Pulse implementations:
Key Players
Companies actively working on Turbine Performance Pulse solutions: