Fault Diagnosis and Condition Monitoring in Wind Energy Systems

Fault diagnosis and condition monitoring constitute the cornerstone of reliable and cost-effective wind energy production. By continuously assessing the operational state of turbines, these practices detect performance degradations and incipient faults in critical components such as gearboxes, bearings, generators and control actuators. Techniques range from vibration and acoustic analyses to power-signal monitoring through SCADA (Supervisory Control and Data Acquisition). Advanced signal processing methods extract features indicative of mechanical wear or electrical anomalies, while data-driven algorithms—including machine learning and statistical hypothesis testing—classify faults and predict their progression. Prognostic tools estimate remaining useful life (RUL), enabling maintenance to be scheduled before failures lead to downtime or catastrophic damage. Integration of multiple sensor streams and fusion of electrical, mechanical and environmental data enhance diagnostic accuracy. The global growth of wind fleets and the push towards offshore installations with limited access underscore the need for robust, remote and online monitoring frameworks. By reducing unscheduled maintenance and maximising availability, these methodologies support the economic and environmental imperatives of large-scale renewable energy deployment.

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Fault Diagnosis and Condition Monitoring in Wind Energy Systems publication trend

The graph below shows the total number of articles in fault diagnosis and condition monitoring in wind energy systems across all publications each year (not limited to Nature Index journals).

Technical terms

Condition monitoring: Ongoing measurement and analysis of system parameters (vibrations, temperatures, electrical signals) to assess health and detect anomalies.

Fault diagnosis: Process of identifying, localising and characterising component malfunctions or degradations within a wind turbine.

Prognostics: Prediction of remaining useful life (RUL) or time to failure based on degradation trends and statistical models.

SCADA: Supervisory Control and Data Acquisition system used for collecting operational data and issuing control commands in real time.

Vibration spectrum analysis: Technique to decompose vibration signals into frequency components, revealing characteristic fault signatures such as bearing defects or gear tooth damage.

Machine learning classifier: Algorithm that learns from historical data to categorise operational states as healthy or faulty, often using support vector machines or neural networks.

References

1. Online condition monitoring and fault diagnosis in wind turbines: A comprehensive review on structure, failures, health monitoring techniques, and signal processing methods. Green Technologies and Sustainability (2025).
2. An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems. Processes (2021).
3. Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss. Sensors (2020).