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Challenge
Wind turbines contain over ten thousand components. Mechanical issues show patterns. Electrical failures do not. Signals shift rapidly with little warning, creating a narrow window to detect faults.
Legacy maintenance processes relied on fixed schedules, resulting in unnecessary downtime or unexpected failures.
The challenge was to build a system capable of collecting multi-source sensor data, analysing real-time patterns, and predicting electrical component failures before they occur.
Connectivity constraints across offshore environments added complexity. Data had to be captured reliably despite intermittent networks, low bandwidth, and long transmission distances.
Key Objectives
- Build an integrated, cloud-native predictive maintenance platform
- Collect vibration, electrical, temperature, humidity and movement data from distributed sensors
- Process data with low latency to detect early electrical fault signatures
- Enable API-first connectivity across heterogeneous hardware
- Visualise asset health from global level down to individual components
- Automate maintenance scheduling based on predicted faults
- Ensure scalability for thousands of turbines across multiple regions
Solution
- A unified industrial IoT and AI stack was engineered to deliver full lifecycle monitoring.
- A distributed network architecture was designed to capture sensor inputs across turbines. Data pipelines were built to ingest continuous and time‑windowed streams. Pre-processing rules reduced noise and ensured low‑latency delivery into the analytics layer.
- Machine learning models were trained to identify electrical anomalies. Pattern recognition techniques mapped vibration, electrical drift, and transient signals to specific fault types. The system generated early warnings before catastrophic failure points.
- A cloud-native platform was created to visualize global turbine networks. The interface enabled drill-down navigation from an entire fleet to individual components. Real-time health indicators, trend analytics, and predictive insights were surfaced in a single operational view.
- APIs supported seamless integration with existing systems. Automated scheduling workflows dispatched maintenance tasks based on failure probability, reducing unnecessary downtime.
- The delivery approach followed a product-first consulting model, combining deep engineering research with iterative validation. A CI/CD pipeline ensured rapid experimentation and continuous deployment of models and platform updates.
Results
- Predictive models identified electrical failures before traditional systems could detect anomalies
- Maintenance was scheduled based on likelihood, not fixed intervals, reducing downtime and replacement costs
- Low-latency data pipelines enabled real-time turbine insights across remote, low-connectivity environments
- Scalable architecture supported expansion across additional sites and asset types
- The system strengthened operational resilience, extended asset lifespan and created a foundation for full digital transformation in industrial maintenance
Innovify builds machine learning products on top of industrial and operational data, from sensor pipelines to production models. Explore our AI & ML Development Services and AI Labs, or talk to our team.
