Wind Turbine Condition Monitoring and Fault Diagnosis Techniques

Condition monitoring and fault diagnosis in wind turbines encompass a suite of data-driven and model-based methodologies designed to ensure reliability, safety and cost-effective operation. At their core, these techniques leverage time-series sensor data—most commonly from supervisory control and data acquisition (SCADA) systems—and higher-frequency vibration or acoustic measurements to characterise baseline healthy behaviour and detect deviations indicative of emerging faults. Signal-processing approaches such as principal component analysis and statistical process control establish multivariate thresholds for anomaly detection, while machine learning methods—including support vector machines, neural networks and convolutional architectures—classify fault types or predict remaining useful life. Recent advances in transfer learning and deep learning enable cross-turbine knowledge transfer and automatic feature extraction, reducing dependence on turbine-specific training data. Prognostic algorithms estimate the time to failure for critical components such as main bearings and converters, facilitating planned maintenance and minimising unplanned downtime. Together, these techniques contribute to global decarbonisation efforts by improving turbine availability, reducing operation and maintenance costs and extending asset lifetimes across both onshore and offshore installations.

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Wind Turbine Condition Monitoring and Fault Diagnosis Techniques publication trend

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

Technical terms

Condition monitoring: Continuous tracking of turbine health via sensors and data analysis to detect anomalies before failure.

Fault diagnosis: Identification and classification of specific component faults once anomalies have been detected.

SCADA: Supervisory Control and Data Acquisition system that collects operational parameters such as wind speed, power output and temperatures.

Transfer learning: Technique that reuses knowledge from pretrained models on one turbine or dataset to improve performance on another.

Autoencoder: Neural network that learns compressed representations of input data and reconstructs it to detect deviations from normal behaviour.

Principal component analysis (PCA): Statistical method that reduces data dimensionality by identifying orthogonal axes capturing maximum variance.

Remaining useful life (RUL): Estimated time until a component reaches a predefined failure threshold based on degradation modelling.

Convolutional neural network (CNN): Deep‐learning architecture that applies convolutional filters to capture spatial or temporal patterns in structured data.

References

1. Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review. Energies (2020).
2. Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning. IEEE Access (2020).
3. Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference. Energies (2018).
4. Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy (2019).
5. Transfer learning applications for autoencoder-based anomaly detection in wind turbines. Energy and AI (2024).
6. Deep Learning Method for Fault Detection of Wind Turbine Converter. Applied Sciences (2021).
7. Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data. Sensors (2021).