Intelligent Fault Detection and Maintenance of Wind Turbine Systems

Intelligent fault detection and maintenance of wind turbine systems integrates advanced sensing, data acquisition and analytical algorithms to safeguard turbine performance and minimise operational costs. Turbines today are equipped with an array of sensors—capturing vibration, acoustic emission, temperature and electrical metrics—that feed into condition monitoring systems via supervisory control and data acquisition (SCADA) platforms. Signal-processing techniques and machine-learning models, including deep autoencoders and recurrent neural networks, extract diagnostic features from these multivariate time series, detecting incipient blade delamination, gearbox wear, generator anomalies and ice accretion. Concurrently, reliability engineering methods such as fault tree analysis (FTA) and binary decision diagrams (BDD) quantify component failure probabilities over time and support predictive maintenance scheduling. Emerging paradigms like federated learning enable collaborative model development across geographically dispersed wind farms while preserving data privacy. Collectively, these innovations enhance system resilience, extend component lifetimes and underpin the economic viability of wind energy on a global scale.

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Intelligent Fault Detection and Maintenance of Wind Turbine Systems publication trend

The graph below shows the total number of articles in intelligent fault detection and maintenance of wind turbine systems across all publications each year (not limited to Nature Index journals).

Technical terms

Supervisory Control and Data Acquisition (SCADA): A digital system for remote monitoring and control of turbine operational parameters.

Condition Monitoring System (CMS): An integrated network of sensors and analytics designed to assess equipment health in real time.

Fault Tree Analysis (FTA): A hierarchical, top-down method for identifying the combinations of component faults that lead to system-level failures.

Binary Decision Diagram (BDD): A graph-based data structure that enables efficient evaluation of complex logical expressions in reliability analysis.

Deep Autoencoder: A layered neural network trained to compress and reconstruct input data, thereby learning relevant feature representations.

Federated Learning: A decentralised approach to machine learning in which local models are trained on-site and aggregated centrally to protect data privacy.

References

1. Wind turbine blade icing detection: a federated learning approach. Energy (2022).
2. Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network. Energies (2022).
3. Reliability Dynamic Analysis by Fault Trees and Binary Decision Diagrams. Information (2020).