Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge. Additionally, the complexity of the external environment and the structure of rolling bearings often manifests faults characterized by randomness and fuzziness, hindering the effective extraction of fault characteristics and restricting the accuracy of fault diagnosis. To overcome these problems, this paper presents a novel approach termed constructive Incremental learning-based ensemble domain adaptation (CIL-EDA) approach. Specifically, it is implemented on stochastic configuration networks (SCN) to constructively improve its adaptive performance in multi-domains. Concretely, a cloud feature extraction method is employed in conjunction with wavelet packet decomposition (WPD) to capture the uncertainty of fault information from multiple resolution aspects. Subsequently, constructive Incremental learning-based domain adaptation (CIL-DA) is firstly developed to enhance the cross-domain learning capability of each hidden node through domain matching and construct a robust fault classifier by leveraging limited labeled data from both target and source domains. Finally, fault diagnosis results are obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel ensemble learning. Experimental results demonstrate that our CIL-DA outperforms several domain adaptation methods and CIL-EDA consistently outperforms state-of-art fault diagnosis methods in few-shot scenarios.

提出了一种新颖的构建增量学习的集成域自适应方法（CIL-EDA），通过结合云特征提取方法和小波包分解（WPD），捕捉多重分辨率方面的故障信息的不确定性，进而通过域匹配增强每个隐藏节点的跨域学习能力，构建稳健的故障分类器，最终通过CIL-DA和并行集成学习的多数投票获得故障诊断结果，在少样本场景下，CIL-DA优于其他域自适应方法，CIL-EDA始终优于当前最先进的故障诊断方法。

基于集成域适应的滚动轴承故障诊断的构造增量学习