Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 16 references
Abstract
Early detection of bearing faults in rotating machinery is essential for predictive maintenance. Although deep learning-based methods have achieved strong results in fault diagnosis, they usually require large amounts of labeled data. In industrial settings, however, faulty samples are limited, which restricts the applicability of fully supervised approaches. In this study, a bearing fault diagnosis framework based on unsupervised representation learning is proposed for limited-label scenarios. Firstly, a convolutional autoencoder is trained on raw vibration signals without using labels to learn informative latent representations. After that, these learned representations are classified using only a limited number of labeled samples. The proposed method is evaluated on the CWRU bearing dataset under same-load and cross-load settings with both single and dual-channel inputs. Experimental results show that the proposed framework achieves strong performance under low-label conditions and that the dual-channel setup further improves classification performance.
An improved dynamic pseudo-label learning strategy is developed, improving model generalization by involving more unlabeled data in training, and a noise reduction learning strategy based on self-supervised learning is proposed, extracting invariant features under noise conditions by a new optimization objective.
Ning Zhou, Lei Liu, Yao Cheng et al.· Structural Health Monitoring· 3 citations
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
Edris Shamsulhaq, Fikri Arif Wicaksana· Jambura Journal of Electrica...· 0 citations
Bearings are critical components of rotating machinery, yet reliable fault diagnosis remains challenging under complex conditions due to signal non-stationarity and data scarcity. To address these issues, this paper proposes a diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification. Firstly, the bearing vibration signals are transformed into two-dimensional time-frequency representations by utilizing the continuous wavelet transform. Subsequently, a multi-attention conditional deep convolutional generative adversarial network (MCDCGAN) is employed for conditional augmentation under class imbalance, integrating attention mechanisms and stabilization strategies to generate more reliable samples for minority fault classes. Finally, a dual-branch parallel time-frequency attention network (DPTAN) is designed to jointly learn temporal and spectral feature representations and then fuse them for fault classification. Experimental results on the CWRU and HIT datasets demonstrate that the proposed method achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions.
Sen Zhang, Lei Yan, Zhaodong Liu et al.· Journal of Measurements in E...· 0 citations
The findings demonstrate that the proposed healthy-only self-supervised framework provides an effective and label-efficient approach for rolling bearing anomaly detection and shows promise for predictive maintenance applications where labelled fault data are limited or unavailable.
Syed Sajjad Haider Zaidi, Alex Shenfield, Hongwei Zhang et al.· Processes· 0 citations
In recent years, deep learning-based bearing fault diagnosis models have demonstrated excellent diagnostic performance under ideal experimental conditions. However, in practical industrial scenarios, the domain shift problem caused by data distribution differences severely undermines the generalization capability of such methods, which is particularly pronounced in cross-device bearing fault diagnosis tasks. Although existing transfer learning-based cross-device bearing fault diagnosis models have achieved encouraging progress, the following shortcomings persist. On the one hand, in cross-device scenarios, the sensitivity of time-domain impact features and frequency-domain resonance characteristics to differences in device structures is different, and the available samples from the target device are often limited. However, most models rely on a single feature domain for modeling, which makes it difficult for them to effectively capture the complete physical information that encompasses both fault‑impulse priors and device‑specific response characteristics, thereby limiting their stability and generalization capability. On the other hand, most models require substantial parameter adjustments during the transfer stage, making them highly prone to overfitting to the individual physical characteristics of the target device, thus undermining the effective knowledge already learned from the source domain. To alleviate the aforementioned issues, a time-frequency collaborative cross-device bearing fault diagnosis model based on supervised transfer learning with limited data is proposed. First, a time-frequency collaborative modeling paradigm is designed, which can extract multi-view complementary information from two physical dimensions: time-domain impact response and frequency-domain structural resonance. This information reflects both the essential attributes of fault patterns and the device‑specific response characteristics, thereby enhancing the robustness of the model to variations in device structures and operating conditions. Second, the low-rank adaptation mechanism is introduced to perform lightweight fine-tuning on key parameters of the pretrained model. This mechanism can constrain parameter updates within a low‑rank subspace, enabling the model to fit the device-specific physical responses of the target device while preserving the stability of the knowledge structure learned from the source domain, thus effectively reducing training complexity and the risk of overfitting. Finally, cross-device diagnosis scenarios are constructed through three real-world cases to comprehensively evaluate the performance of the proposed model. Experimental results indicate that the proposed model outperforms some current mainstream models in terms of both diagnostic accuracy and generalization performance, sufficiently demonstrating its effectiveness and practicality in cross-device scenarios.
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Hong Wang, Hai Xue et al.· Engineering Research Express· 0 citations
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