Abstract Rolling bearing fault diagnosis is a meaningful yet challengeable task. To improve the performance of rolling bearing fault diagnosis, this paper proposes an enhanced selective ensemble deep learning method with beetle antennae search (BAS) algorithm. Firstly, multiple deep base models are constructed to automatically capture sensitive features from raw vibration signals. Secondly, to ensure the diversity of the base models, sparse autoencoder, denoising autoencoder and linear decoder are used to construct different deep autoencoders, respectively, and also Bootstrap is used to design distinctive training data subsets for each base model. Thirdly, an enhanced weighted voting (EWV) combination strategy with class-specific thresholds is proposed to implement selective ensemble learning. Finally, BAS algorithm is used to adaptively select the optimal class-specific thresholds. Experimental bearing data are used to verify the effectiveness of the proposed method. The results suggest that the proposed method can more accurately and robustly recognize different kind of faults than both the individual base models and other existing ensemble learning methods.

中文翻译：

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基于甲虫触角搜索算法的滚动轴承故障诊断增强选择性集成深度学习方法

摘要 滚动轴承故障诊断是一项有意义但具有挑战性的任务。为了提高滚动轴承故障诊断的性能，本文提出了一种具有甲虫天线搜索（BAS）算法的增强型选择性集成深度学习方法。首先，构建多个深度基础模型以从原始振动信号中自动捕获敏感特征。其次，为了保证基础模型的多样性，分别使用稀疏自编码器、去噪自编码器和线性解码器构建不同的深度自编码器，并使用 Bootstrap 为每个基础模型设计不同的训练数据子集。第三，提出了一种具有特定类阈值的增强加权投票（EWV）组合策略来实现选择性集成学习。最后，BAS 算法用于自适应地选择最佳的类特定阈值。实验轴承数据被用来验证所提出方法的有效性。结果表明，与单独的基础模型和其他现有的集成学习方法相比，所提出的方法可以更准确、更稳健地识别不同类型的故障。