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.
Abstract
Reliable bearing fault detection is essential for predictive maintenance in industrial systems; however, obtaining labelled fault data is often expensive, time-consuming, and impractical in real-world deployments. To address this challenge, this study proposes a healthy-only self-supervised anomaly detection framework for bearing health monitoring using vibration measurements. The proposed approach combines convolutional neural networks and Transformer-based temporal modelling to learn informative representations from healthy vibration signals without requiring fault labels during representation learning. Three self-supervised learning strategies—reconstruction-based, contrastive, and a unified contrastive–reconstruction objective—are investigated to evaluate the effectiveness of different representation learning approaches. The learned latent representations are subsequently analysed using Isolation Forest and Mahalanobis-distance anomaly scoring methods. To provide a realistic assessment of generalisation, a strict grouped cross-validation protocol is employed, where data are partitioned at the sample level to prevent information leakage between training and testing sets. Furthermore, prevalence-aware experiments are conducted under 5% and 10% fault prevalence scenarios to assess deployment robustness. Experimental results on the Paderborn bearing dataset demonstrate that the proposed CNN + Transformer model trained with combined contrastive and reconstruction objectives and evaluated using Isolation Forest achieves the best overall performance, obtaining a ROC-AUC of 0.878±0.015, a PR-AUC of 0.958±0.005, and an F1-score of 0.590±0.040. The results consistently outperform classical feature-based approaches, One-Class SVM, and autoencoder baselines. Ablation analysis further shows that combining contrastive and reconstruction objectives produces more informative representations than either objective alone. 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.
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
Structural Health Monitoring (SHM) increasingly relies on data-driven methods applied to dense vibration measurements, however, reliable damage detection from acceleration time series remains challenging due to environmental variability, limited labeled damage data, and severe class imbalance.
This work investigates self-supervised learning for anomaly detection in bridge acceleration signals. First, we adopt a contrastive framework that captures temporal and contextual dependencies to pretrain an encoder exclusively on fixed-length windows of data collected under healthy conditions. To account for real-world variability, physics-informed data augmentations are introduced to simulate measurement disturbances such as sensor noise and signal variations. Following pretraining, anomalies are identified by measuring deviations of streamed windows from the learned representation of healthy structural behavior, enabling damage detection without explicit labels or predefined damage categories. Experiments on the RT345 multi-scenario bridge benchmark show that the learned representations achieve superior separation between healthy and damaged windows compared to classical baselines and reconstruction-based deep autoencoders.
Finally, we evaluate an online post-processing strategy that aggregates consecutive anomaly scores to emulate streaming deployment. While this aggregation improves detection sensitivity (achieving a True Positive Rate above 96%), it can increase false alarms when using a fixed operating point. These findings indicate that the proposed contrastive-learning-based approach enables effective damage detection from vibration signals in an online setting.
Mohamed Abdellilah Fidma, J. Bercher, Franziska Schmidt· e-Journal of Nondestructive...· 0 citations
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.
Ahmet Kaplan, Kürşat İnce, Murat Beken· Signal Processing and Commun...· 0 citations
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
Smart fault diagnosis is a crucial component of modern industrial systems, offering early detection of machine conditions to prevent costly breakdowns and safety issues. While state-of-the-art deep learning models are capable of near-perfect classification on controlled laboratory data, they often perform poorly in practice because of noisy environments, class imbalance and distribution shifts across operating conditions. This paper addresses these challenges by re-formulating industrial acoustic monitoring as a supervised image classification problem with superlet time-frequency representations, which capture both the transient dynamics and phase-envelope. An efficient CNN-Transformer hybrid model is proposed by placing a lightweight Transformer encoder on an ImageNet-pretrained ResNet-50 backbone with spatial attention gating to learn both local and global spectro-temporal features. The proposed architecture is lightweight, with 25.6M parameters and 4.1G FLOPs, supporting efficient resource usage. An asymmetric focal contrastive learning strategy is also proposed to improve anomaly discrimination under extreme imbalance. Experimental results on the MIMII dataset demonstrate that the proposed framework achieves 94.82% accuracy, 91.08% balanced accuracy, 91.72% F1-score, and 0.9758 ROC-AUC under a strict group-aware evaluation protocol, while requiring only 25.6M parameters, 4.1G FLOPs, and 4.5 ms inference time per sample. In cross-domain evaluation on the CWRU dataset, the model further achieves a zero-shot AUC of 0.8886 and a fine-tuned AUC of 1.0000 within three epochs. These results validate that the proposed framework provides an effective, robust, and generalizable solution for industrial fault diagnosis.
S. Anik, Md. Ehsanul Haque, Fahmid Al Farid et al.· Scientific Reports· 0 citations
Reliable bearing fault diagnosis is essential for predictive maintenance of rotating machinery, particularly in applications where contact-based vibration sensors are difficult to install or maintain. This study proposes an explainable acoustic fault diagnosis framework based on Mel-spectrogram representations and a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) architecture. Acoustic emission signals collected from a controlled bearing fault simulator are first segmented and transformed into Mel-spectrograms to represent the time–frequency structure of normal and faulty bearing conditions. The CNN module extracts localized spectral patterns from the Mel-spectrogram images, while the GRU module models temporal dependencies along the spectrogram time axis with fewer recurrent parameters than conventional LSTM-based designs. To address the interpretability limitations of deep learning models, Grad-CAM, Integrated Gradients, and SHAP are employed to analyze the frequency–time regions contributing to the model decisions. In addition, the explanation maps are compared with fault-relevant spectral regions derived from fault characteristic frequency analysis to evaluate whether the model focuses on physically meaningful patterns rather than arbitrary image regions. The proposed framework achieved an high accuracy of in the initial experiment and was further evaluated through repeated validation to assess performance stability under small-sample conditions. The results demonstrate that acoustic sensing combined with explainable CNN–GRU learning can provide a non-contact and interpretable alternative for bearing fault diagnosis. However, the limited dataset size remains an important constraint, and future studies should validate the framework on larger record-level and cross-domain datasets.
Emrah Aslan, Yıldırım Özüpak· Proceedings of the Instituti...· 1 citation
