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
A diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification and achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions is proposed.
Sen Zhang, Lei Yan, Zhao-Dong 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· 1 citation
A time-frequency collaborative cross-device bearing fault diagnosis model based on supervised transfer learning with limited data that outperforms some current mainstream models in terms of both diagnostic accuracy and generalization performance, sufficiently demonstrating its effectiveness and practicality in cross-de...
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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