Cloud ensemble learning for fault diagnosis of rolling bearings with stochastic configuration networks

The scarcity of fault samples poses a significant challenge for fault diagnosis of rolling bearings in industrial environment. Conventional fault diagnosis methods struggle to achieve satisfactory results in the few-shot scenarios. Furthermore, common entropy-based feature extraction methods fail to adaptively represent the uncertainty information hidden in vibration signals. To overcome these issues, this article proposes a stochastic configuration network-based cloud ensemble learning (SCN-CEL). Firstly, a cloud feature extraction method is developed to effectively capture fault information from vibration signals while accounting for their inherent uncertainty based on backward cloud generator (BCG) of cloud model (CM), without requiring hyperparameter settings. Subsequently, a cloud oversampling (COS) method is proposed to augment the feature space of limited samples and generate sufficient samples for improving diagnostic accuracy based on bidirectional cloud generator. Finally, we introduce an ensemble model that combines SCNs with multiple constrained COS to comprehensively characterize uncertain fault information and advance the generalization of diagnosis machine. By harnessing the constructive incremental learning of SCN, SCN-CEL guarantees both efficient modeling and accurate prediction for bearing fault diagnosis. Extensive experiments evaluate the effectiveness of each module in SCN-CEL and demonstrate its favorable performance in distinguishing fault categories of rolling bearings in the few-shot scenarios.