Rotor Imbalance Classification in Wind Turbines Using Multichannel Vibration Analysis and a DWT–LDA Framework

Enhanced Rotating Machinery Fault Diagnosis Using Holo-Hilbert Spectrum Analysis and Machine Learning

Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions. Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44]. Author Contribution Statement Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.

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

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

Wind turbine blade faults, such as surface erosion, cracks, mass imbalance, and twist deformation, significantly compromise operational efficiency and reliability, thereby increasing maintenance costs. This research presents an artificial neural network (ANN)-based diagnostic approach for identifying five distinct fault states in wind turbine blades using vibration signal data collected at a constant operational speed of 1.3 m/s. The dataset, which encapsulates real-world vibration responses under varying fault conditions, was analyzed to extract amplitude features for classification. A balanced dataset of 500 samples per class was used to ensure robust training and evaluation. The ANN model achieved highly reliable performance, with classification accuracies (CA) of 96.21% (crack), 97.12% (erosion), 95.47% (healthy), 96.04% (twist deformation), and 94.38% (mass imbalance). Corresponding F1-scores were 95.32%, 96.51%, 94.45%, 95.18%, and 93.36%, respectively. These results confirm the model's effectiveness in distinguishing between common wind turbine blade faults and healthy conditions. This study demonstrates the potential of ANN-based systems for intelligent fault detection in wind energy systems, aiding in the advancement of condition-based maintenance and operational safety.

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

The increasing demand for reliability in wind turbine systems makes early bearing fault detection under variable-speed conditions a persistent challenge. This paper proposes a hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach. Synthetic vibration signals are generated through logistic-curve interpolation with pink noise, enabling controlled degradation simulation. Features from time, frequency, and time–frequency domains were ranked using Mutual Information, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was employed to identify progressive wear stages. Cluster centers serve as anchors for mapping degradation into RUL percentages, while classification ensures stage consistency. Experimental results demonstrate six well-defined clusters for inner-race faults (Silhouette 0.5190), three moderate clusters for outer-race faults (0.2339), and overlapping patterns for rolling element faults (–0.1527), with zero RUL deviation in the best case. The proposed framework combines real and synthetic data to enhance generalization while reducing computational cost, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.

Severity Classification of Progressive Rotor Electrical Unbalance in Wind Turbine DFIGs Using Deep Temporal Learning

Rotor electrical unbalance (REU) in doubly-fed induction generators (DFIG) used in wind energy conversion systems is a progressive fault whose early detection remains challenging under variable-speed operating conditions. This study presents a comprehensive hybrid framework integrating classical signal processing with advanced deep learning for robust, multi-severity REU detection. The framework operates in three stages. In the first stage, spectral analysis via the Fast Fourier Transform (FFT) extracts fault-indicative sideband amplitudes at (1±2ks)f1; Hilbert-transform envelope analysis captures amplitude modulation patterns; and four statistical indicators (kurtosis, crest factor, skewness, RMS) quantify signal impulsiveness. In the second stage, the Robust Local Mean Decomposition (RLMD) decomposes the stator current into Product Functions (PFs), from which Fault Energy Ratios (FER) are computed as dimensionless, load-invariant descriptors. These stages form a compact eleven-dimensional feature vector. The third stage introduces temporal modelling via LSTM networks and a hybrid CNN-LSTM. Experimental validation on a 30 kW laboratory DFIG test bench with four REU severity levels (Healthy, 150%, 225%, 300%) under 5-fold cross-validation yields 98.0% accuracy and 97.7% macro-F1 for the CNN-LSTM. Inference time is 4.2 ms/sample on CPU.

Machinery Fault Detection via Multi-Domain Features and ML–DL Stacking

Overall, the results indicate that multi-domain feature extraction provides an effective compact representation for machinery fault diagnosis, while RF-based models offer the most reliable balance between classification performance, stability, and computational efficiency.