Wind turbines operate in harsh and dynamic environments where component failures can lead to costly downtime, reduced energy output, and high maintenance expenses. Traditional maintenance methods—scheduled inspections or manual monitoring—fail to detect early signs of failure and often result in reactive interventions.
PySquad builds advanced predictive maintenance systems powered by machine learning and edge AI. Our solutions analyse real-time sensor data, detect anomalies, predict component failures, and deliver actionable alerts to operators. This helps wind farm owners improve reliability, reduce O&M costs, and maximise turbine efficiency.
Problem Businesses Face
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Unexpected turbine failures leading to production losses.
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Difficulty monitoring large fleets of turbines across multiple sites.
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Manual inspections that miss early warning signs.
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High operational expenditure due to reactive maintenance.
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Inability to analyse vast sensor datasets in real time.
Our Solution
PySquad creates ML + Edge AI systems designed specifically for wind turbine predictive maintenance.
Our solution includes:
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IoT and SCADA data ingestion from turbines (vibration, temperature, acoustics, RPM, weather).
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Machine learning models for early fault prediction.
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Edge AI deployment for on-device or near-device analysis.
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Dashboards with turbine health scores, alerts, and trends.
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Automations for maintenance scheduling and technician workflows.
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Reporting for O&M teams and asset managers.
Key Features
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Real-time vibration and acoustic anomaly detection.
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Failure prediction for bearings, gearboxes, blades, and generators.
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Edge AI inference for low-latency fault detection.
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Sensor fusion from wind speed, direction, and mechanical data.
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Health scoring models and failure probability indicators.
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Maintenance trigger automation and ticketing integration.
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Multi-turbine monitoring dashboards with map view.
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Historical trends for root cause analysis.
Benefits
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Reduced downtime and fewer unexpected failures.
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Lower maintenance costs through condition-based servicing.
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Longer equipment lifespan and improved reliability.
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Early detection of minor issues before they escalate.
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Increased energy production and revenue stability.
Why Choose PySquad
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Expertise in ML, IoT, renewable analytics, and edge computing.
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Experience handling large-scale turbine datasets.
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Human-first dashboards for technicians and managers.
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Secure, scalable architecture suitable for multi-site wind farms.
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End-to-end delivery from sensors to AI to cloud dashboards.
Call to Action
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Want to predict turbine failures before they happen?
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Need real-time turbine health analytics?
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Looking for ML + Edge AI experts for wind operations?
Partner with PySquad to build predictive maintenance systems that keep your turbines running at peak performance.