Self-Supervised CNN–Transformer Anomaly Detection for Bearing Health Monitoring

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.