A Unified Deep Neural Network Framework With Granular Ball Embedding for Imbalanced Fault Diagnosis: Design and Analysis

Mainstream strategies for addressing imbalanced monitoring data distributions, such as data sampling and cost-sensitive learning methods, face multiple challenges, including data heterogeneity, uncertainty, biased attention toward normal or fault classes, and reduced discriminability of fault features. These issues collectively lead to a degradation in fault diagnosis (FD) performance. To overcome the aforementioned bottlenecks, this article proposes a unified FD framework, termed a granular computing.based deep neural network (GDNN), which is designed from the novel perspective of granular ball (GB) embedding. Within this framework, the input data are first transformed into suboptimal latent features through a generic deep neural network (DNN).based feature extraction component. Subsequently, an information granulation component reorganizes these features into class-specific local neighborhood structures in the form of GBs. Finally, a Softmax-based fully connected classifier performs FD. The GDNN framework supports end-to-end learning by minimizing a composite loss function consisting of the cross-entropy loss and the justifiable granularity (JG) loss. The main contribution of this work lies in a novel GB embedding mechanism that guides DNNs to focus on GB-based structures rather than isolated individual samples in imbalanced scenarios. Each class is represented by a GB that encapsulates its structural knowledge in the latent feature space, thereby ensuring consistent attention across classes and effectively mitigating severe overfitting. Experimental results on two industrial processes demonstrate that the proposed framework performs robustly across diverse imbalanced scenarios and adapts well to different DNN architectures, providing a novel perspective and a generalizable paradigm for imbalanced FD.