Bearing faults are one of the most common inducements for machine failures. Therefore, it is very important to perform bearing fault diagnosis reliably and rapidly. However, it is fundamental but difficult to extract impulses buried in heavy background noise for bearing fault diagnosis. In this paper, a novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed. The algorithm is based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults. Moreover, a periodic prior is embedded and updated dynamically through each iteration of the optimization procedure. Additionally, we formed a deterministic rule about how to set the parameters adaptively. The main advantage over conventional sparse representation methods is that AdaESPGL is parameter free (forming a deterministic rule) and rapid (extracting the impulsive information directly from the time domain). Finally, the performance of AdaESPGL is verified through a series of numerical simulations and the diagnosis of a motor bearing. Results demonstrate its superiority in extracting periodic impulses in comparison to other state-of-the-art methods.

中文翻译：

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用于轴承故障诊断的增强型稀疏周期组套索

轴承故障是机器故障最常见的诱因之一。因此，可靠、快速地进行轴承故障诊断非常重要。然而，提取隐藏在沉重背景噪声中的脉冲用于轴承故障诊断是基本但困难的。在本文中，提出了一种用于轴承故障诊断的新型自适应增强稀疏周期组套索（AdaESPGL）算法。该算法基于所提出的增强稀疏组套索惩罚，提高了轴承故障脉冲特征的组内和组间稀疏性。此外，通过优化过程的每次迭代动态地嵌入和更新周期性先验。此外，我们形成了关于如何自适应设置参数的确定性规则。与传统稀疏表示方法相比，AdaESPGL 的主要优点是无参数（形成确定性规则）且快速（直接从时域中提取脉冲信息）。最后，通过一系列数值模拟和电机轴承诊断验证了 AdaESPGL 的性能。结果表明，与其他最先进的方法相比，它在提取周期性脉冲方面具有优势。