Aug 2026· Journal of Dynamics Monitoring and Diagnostics· 0 citations· 2 references
TL;DR
Inspired by few-shot learning, a support–query task construction strategy is introduced, reformulating classification as conditional task reasoning and a global coverage and local balance task sampling strategy is designed to enhance task diversity and mitigate sample imbalance.
Abstract
Wind turbine gearbox fault diagnosis is essential for ensuring the safe and stable operation of wind energy systems. However, in industrial scenarios, limited fault data and highly complex, non-stationary operating conditions lead to significant distribution shifts, which reduce the generalization ability of traditional methods. To address this issue, we propose a task-oriented fault diagnosis framework based on large language models. Time-domain and frequency-domain features are extracted from vibration signals to construct structured representations. Inspired by few-shot learning, a support–query task construction strategy is introduced, reformulating classification as conditional task reasoning. Low-Rank Adaptation is adopted for parameter-efficient fine-tuning of the pre-trained model. In addition, a global coverage and local balance task sampling strategy is designed to enhance task diversity and mitigate sample imbalance. Experiments on a wind turbine gearbox dataset show consistent performance across multiple backbone models, including LLaMA2-7B, LLaMA3-8B, Qwen-7B, and Baichuan-7B. LLaMA3-8B achieves the best performance, with 98.00% accuracy and an F1-score of 0.9798. These results demonstrate strong robustness and cross-model generalization under few-shot and complex operating conditions.
Compound fault diagnosis of wind turbine gearboxes (WTGs) has received extensive attention. Existing deep learning-based methods typically require sufficient compound fault samples for model training. However, collecting such samples is extremely difficult and often impractical in real-world industrial scenarios. Inspired by zero-shot learning, this paper proposes a semantic knowledge transfer framework to diagnose compound faults using only single-fault data for training. Within this framework, a semantic knowledge library is first constructed to encode human expert intelligence into high-fidelity knowledge vectors, establishing a shared semantic space for all fault classes. To ensure signal representations align with these expert semantics, a time-frequency informative perceptron is introduced to capture comprehensive fault signatures by simultaneously capturing discriminative features from both time and frequency domains. Finally, imbalance-robust knowledge learners are designed to bridge the gap between physical features and knowledge labels while mitigating the inherent class imbalance effects. The proposed framework is validated on a self-built WTG compound fault test platform. Experimental results showcase its exceptional effectiveness and superiority in recognizing unseen compound faults, providing a robust solution for mechanical fault diagnosis under zero-sample scenarios.
Unknown authors· Structural Health Monitoring· 0 citations
INTRODUCTION: Offshore wind turbine gearboxes operate under complex conditions and are highly prone to faults. Traditional single-source diagnostic methods are sensitive to noise and load fluctuations, limiting diagnostic reliability.
OBJECTIVES: This study aims to improve the diagnostic accuracy and recognition performance of gearbox fault categories.
METHODS: Multi-source monitoring data were preprocessed and fused, followed by feature optimization and construction of a deep learning-based diagnosis model for fault detection and classification.
RESULTS: The proposed model achieved accuracies of 0.951, 0.947, and 0.938 under different load conditions, with Area Under the Curve AUC values above 0.96, outperforming benchmark models.
CONCLUSION: The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.
Hongfeng Chen, Chao Chen, Xingdu Li et al.· EAI Endorsed Transactions on...· 0 citations
A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.
Fanlong Zhu, Junyu Lai, Peiwen Lu et al.· Advances in Mechanical Engin...· 0 citations
In this study, a hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics. In the time domain, convolutional layers combined with an Enhanced Gated Attention (EGA) mechanism emphasize informative signal components while suppressing noise. Temporal evolution is modeled using Neural Ordinary Differential Equations (Neural ODEs), enabling smooth and stable continuous-time feature representations. In parallel, a Fourier Neural Operator (FNO) extracts frequency-domain characteristics, augmented with gated attention to focus on fault-related spectral patterns. Long Short-Term Memory (LSTM) layers capture long-range dependencies, while Squeeze-and-Excitation (SE) blocks adaptively recalibrate channel-wise feature responses. A multi-scale attention-based fusion module integrates domain-specific representations and auxiliary features to enhance discrimination under varying operating conditions. The proposed model is evaluated on the SUBFv1.0 dataset through extensive ablation studies and experiments under multiple noise levels, achieving 98.41% accuracy in noise-free conditions and maintaining performance above 91% even at 5 dB SNR. Unlike existing multi-path approaches that combine heterogeneous features in a loosely coupled or discrete manner, the proposed architecture uniquely integrates multi-domain feature learning with bidirectional attention mechanisms and continuous-time temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur· Information Technology and C...· 0 citations
Rolling bearings are critical components in rotating machinery, and their failures may cause unexpected downtime and safety risks. However, conventional deep diagnostic models are often difficult to deploy on resource-constrained edge devices because of their high computational cost and memory consumption. This paper proposes a lightweight time–frequency attention network (LTFANet) for multi-fault diagnosis of rolling bearings on an edge platform. The proposed model directly processes one-dimensional vibration signals and employs multi-scale depthwise separable convolutions to capture impact and periodic fault features with low computational complexity. A lightweight frequency branch is introduced to enhance fault-frequency representation, while an efficient channel attention module adaptively emphasizes fault-sensitive features. Moreover, a severity-aware multi-task extension is introduced to jointly identify the fault location and degradation level. To further improve edge inference efficiency, knowledge distillation, structured pruning, and TensorRT-based acceleration are integrated into the deployment pipeline. Experiments on CWRU-10 and Paderborn achieve 97.20% and 90.25% accuracy, respectively, while LTFANet contains only 0.020 M parameters and requires 0.610 M FLOPs. Knowledge distillation increases the CWRU-10 accuracy to 98.50%, and the severity-aware extension achieves 95.18% severity accuracy. On the NVIDIA Jetson Nano, the pruned TensorRT FP16 implementation achieves an average inference latency of 0.520 ms and a throughput of 1923.08 samples/s. The framework provides an effective solution for real-time and low-cost bearing condition monitoring at the edge.