Vibration monitoring using sensors mounted on machines is widely used for rotating machinery fault diagnosis. The periodic overlapping group sparsity (POGS) method has been developed in previous work of authors, and is an effective technique for detecting faults induced in rotating machines. However, the regularization parameter of the POGS problem is roughly specified via a look-up table provided in the original work. To address this problem, a data-driven diagnostic method, which is termed the adaptively regularized periodic overlapping group sparsity (ARPOGS), is proposed in this paper. The non-stationary fault feature ratio which is defined in the Hilbert domain is employed to guide the optimal regularization parameter. The criterion of setting the interval of candidate regularization parameters is also discussed. The ARPOGS is developed in terms of a convex optimization problem, while non-convex regularizations are used to further promote the sparsity. Since the non-convex ...

中文翻译：

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数据驱动的群组稀疏特征提取方法在风机传输系统故障检测中的应用

使用安装在机器上的传感器进行的振动监测广泛用于旋转机械故障诊断。周期性重叠群稀疏性（POGS）方法是作者先前的工作中发展起来的，是检测旋转机器中引起的故障的有效技术。但是，POGS问题的正则化参数是通过原始工作中提供的查找表粗略指定的。为了解决这个问题，本文提出了一种数据驱动的诊断方法，称为自适应正则化的周期性重叠群稀疏性（ARPOGS）。使用希尔伯特域中定义的非平稳故障特征比率来指导最优正则化参数。还讨论了设置候选正则化参数间隔的准则。ARPOGS是根据凸优化问题开发的，而非凸正则化则用于进一步提高稀疏性。由于非凸...