Normal-Faulty Adversarial Bridging Health Assessment for Rolling Bearing Without Prior Faults

In many real-world industrial scenarios, health assessment of bearings with no prior faults (BNFs) is hindered by label-insufficient and sample-insufficient problem. To address this issue, this article proposes a novel normal-faulty adversarial bridging framework (NFABF) to unify three types of samples—normal, faulty, and suspicious—from multibearing models into a common latent space, thereby facilitating real-time BNF health assessment. Specifically, an adversarial bridging autoencoder is devised to simultaneously reconstruct normal samples and “deconstruct” faulty samples, while employing an information-entropy (IE)–based method to refine pseudolabels for suspicious samples. This process enables the feature encoder to effectively embed suspicious samples into the transition region between normal and faulty features. Furthermore, a bridging alignment strategy, integrating both maximum mean discrepancy and feature cohesion loss, is introduced to reduce discrepancies among the three sample types, while a temporal continuity constraint enforces a realistic degradation evolution. Last, a health index is constructed by combining reconstruction error and fault probability, and a regression model is incorporated to estimate the remaining useful life. The proposed framework is validated using real main shaft bearing data from wind turbine systems and extensively compared with multiple baselines. Experimental results demonstrate that the NFABF achieves superior performance in bearing health assessment for the BNF scenario.