Ensemble learning method has shown its superiority in bearing fault diagnosis based on the condition based monitoring. Nevertheless, features extracted from the monitoring signals of bearing systems often contain interrelated and redundant components, leading to poor performances of the base classifiers in the ensemble. Moreover, the current ensemble methods rely on voting strategies to aggregate the diagnostic predictions of these base classifiers without considering their reliabilities and weights simultaneously. To address the aforementioned issues, we propose a novel Diagnosis Aggregation Method with Evidential Reasoning rule, i.e., DAMER, for bearing fault diagnosis. In this method, a semi-random subspace approach using a structured sparsity learning model is developed to decrease the negative effect of interrelated and redundant features, and in the meanwhile to generate accurate and diverse base classifiers. Furthermore, an adaptive evidential reasoning rule (ER rule) incorporating with ensemble learning theory is utilized to aggregate the diagnostic predictions of the base classifiers by taking both their weights and reliabilities into account. To validate the proposed DAMER, an empirical study is conducted on Case Western Reserve University bearing vibration dataset, and the experimental results verify the effectiveness of the proposed DAMER as well as its superiority over commonly used ensemble methods. The performances of feature subsets from multiple domains and the aggregation capability of the adaptive ER rule were also investigated. Results illustrate that DAMER can be utilized as an effective method for bearing fault diagnosis.

基于状态监测的集成学习方法已显示出其在轴承故障诊断中的优越性。然而，从轴承系统的监视信号中提取的特征通常包含相互关联和冗余的组件，从而导致集合中基本分类器的性能较差。此外，当前的集成方法依靠投票策略来汇总这些基本分类器的诊断预测，而无需同时考虑其可靠性和权重。为了解决上述问题，我们提出了一种新的带有证据推理规则的诊断集合方法，即DAMER，用于轴承故障诊断。在这种方法中，开发了一种使用结构化稀疏性学习模型的半随机子空间方法，以减少相互关联和冗余特征的负面影响，同时生成准确而多样的基础分类器。此外，结合整体学习理论的自适应证据推理规则（ER规则）被用于通过综合考虑基础分类器的权重和可靠性来汇总其基础的诊断预测。为了验证提出的DAMER，对Case Western Reserve University轴承振动数据集进行了实证研究，实验结果证明了提出的DAMER的有效性以及其优于常用集成方法的优越性。还研究了来自多个域的特征子集的性能以及自适应ER规则的聚合能力。结果表明，DAMER可以用作轴承故障诊断的有效方法。