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.

翻译：鉴于滚动轴承故障诊断作为不同工况下的实际问题具有普遍性，有限样本的可用性加剧了该挑战。此外，外部环境与滚动轴承结构的复杂性常导致故障呈现随机性与模糊性特征，阻碍了故障特征的有效提取并限制了故障诊断的准确性。为解决上述问题，本文提出一种名为构造增量学习集成域自适应（CIL-EDA）的新方法。具体而言，该方法基于随机配置网络（SCN）实现，以构造性方式提升其在多域环境中的自适应性能。具体地，采用云特征提取方法结合小波包分解（WPD），从多分辨率角度捕捉故障信息的不确定性。随后，首次提出基于构造增量学习的域自适应（CIL-DA）方法，通过域匹配增强每个隐节点的跨域学习能力，并利用目标域与源域的有限标记数据构建鲁棒的故障分类器。最后，通过集成CIL-DA与并行集成学习的CIL-EDA进行多数投票获得故障诊断结果。实验表明，我们的CIL-DA优于多种域自适应方法，且CIL-EDA在少样本场景中持续优于最先进的故障诊断方法。