Artificial Neural Network-Based Diagnosis of Wind Turbine Blade Faults Using Vibration Analysis at Constant Operational Speed

Hybrid Energy Distance and Permutation Testing for Vibration-Based Wind Turbine Blade Fault Diagnosis

Wind turbine blade failure diagnosis is of critical importance, as the inability to reliably detect such faults can result in substantial financial losses, prolonged downtime, extensive repair activities, as well as increased operation and maintenance costs. This paper introduces a new application of energy distance and permutation testing for wind turbine blade fault diagnosis based on vibration data analysis. The proposed approach combines a non-parametric energy distance and permutation testing for the identification and characterization of blade faults in wind turbines. Unlike conventional approaches, this method does not require the assumption of a normal data distribution, which is particularly important when analysing vibration signals, as such data often deviates from normality. The approach is also robust against the presence of outliers. The energy distance metric is employed to classify the blade condition of the wind turbine and to investigate subtle changes in blade behaviour by comparing healthy and corresponding faulty states while considering the entire data distribution rather than conventional summary statistics. In the second stage, a permutation test is applied to statistically validate the energy distance results via p-values. The proposed method is validated using experimental vibration datasets representing healthy and faulty blade conditions, including cracked, eroded, twisted, and imbalanced faults. These datasets are analysed under varying wind speed conditions to assess the robustness of the proposed method under different operating conditions. In the second stage, the permutation test confirms the statistical significance of the detected distributional differences, providing additional confidence in the diagnostic results. The findings indicate that the combined use of energy distance and permutation testing effectively identifies and detects the faults from the healthy blade behaviour across two different wind speed conditions. The findings of this study indicate the potential of the proposed approach as a simple, robust, and interpretable solution for wind turbine blade fault diagnosis based on vibration data analysis.

Fault diagnosis of small sample wind turbine blade in ice-covered and damage condition based on ResNet50-SVM and transfer learning

Wind turbine blade failures, such as icing and damage, risk safety and efficiency, but limited fault data hinders diagnosis. This study proposes a hybrid framework combining ResNet50-SVM and transfer learning for small-sample fault diagnosis. A coupled simulation model first generates comprehensive dynamic fault data. Vibration signals are converted into time-frequency images via frequency-sliced wavelet transform, then classified by the ResNet50-SVM model. Next, transfer learning adapts simulated pre-trained models to target turbines using minimal samples. Results show the ResNet50-SVM model outperforms LSTM approaches, achieving up to 95.24% accuracy and improving precision and recall by 15–30%. Furthermore, transfer learning improved recall by 20–40% using only 4 to 6 target samples. Ultimately, this scalable simulation-to-reality approach enhances wind farm maintenance efficiency and reduces economic losses.

A PINN-Based Fault Diagnosis Method for Crack Damage in Wind Turbine Blades

Vibration response analysis constitutes a pivotal approach for crack monitoring and early warning of damage identification in wind turbine blades. Traditional data-driven methods, however, demonstrate marked deficiencies in identification accuracy and generalization capability. To mitigate these issues, a method for crack damage identification in wind turbine blades is proposed, grounded in Physics-Informed Neural Networks (PINNs). Initially, utilizing a scaled-down test platform for doubly fed wind turbines, simulation experiments on blade cracks were executed. Vibration data were amassed under varying crack locations and lengths to scrutinize the intrinsic relationship between crack characteristics and the three-dimensional vibration response of the blade root bearing pedestal. Subsequently, leveraging the rotating cantilever Euler–Bernoulli beam model, the physical correlation between cracks and vibrations was dissected, and a physical information constraint model was formulated. This model was then amalgamated with a GRU-Transformer network to establish a PINN model tailored for crack damage identification. Ultimately, the model underwent testing and validation utilizing experimental data. The outcomes reveal that, in comparison to traditional data-driven models, the PINN model exhibits superior accuracy and precision in crack identification and localization, along with exceptional generalization capability and noise resilience. This research provides a novel technical pathway for enhancing the intelligence level of health monitoring for wind turbine units and holds substantial engineering significance for achieving precise condition assessment and early fault warning.

Prediction and Classification of Residual Service Life in Wind Turbine Bearings Under Variable Speed Conditions Using Hybrid Machine Learning Models

A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.

Detection of wind turbine hub bolt drop noise based on fusion acoustic features classification

Data-Driven Fault Diagnosis of Rolling Bearings Using Neural Networks Optimized via Bayesian, Particle Swarm, and Genetic Algorithm Methods with Time–Frequency Features

This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks optimized through Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.