Rolling bearing is widely used in rotating machinery and, at the same time, it is easy to be damaged due to harsh operating environments and conditions. As a result, rolling bearing is critical to the safe operation of the machinery devices. Compound fault of rolling bearing is not a simple superimposition of multiple single faults, but the coupling of multiple fault features, making the vibration signal, becomes complicated. In our study, sparsity-oriented nonconvex nonseparable regularization (SONNR) method is proposed to rolling bearing compound fault diagnosis under noisy environment. Firstly, a theoretical model of rolling bearing compound fault is established, and the vibration characteristics of rolling bearing compound fault are analyzed. Secondly, four-layer structure of the SONNR method is proposed: input layer, nonconvex sparse regularization layer, signal reconstruction layer, and compound faults isolation layer. Finally, the validity of the method is verified by simulation data and actual data, and it is compared with the traditional time domain diagnostic methods and artificial intelligence methods.

中文翻译：

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嘈杂环境下滚动轴承复合故障的稀疏性非凸不可分正则化

滚动轴承广泛用于旋转机械，同时，由于恶劣的工作环境和条件，很容易损坏。因此，滚动轴承对于机械设备的安全运行至关重要。滚动轴承的复合故障不是多个单个故障的简单叠加，而是多个故障特征的耦合（使振动信号变得复杂）。在研究中，提出了稀疏导向的非凸不可分正则化（SONNR）方法，用于嘈杂环境下滚动轴承复合故障的诊断。首先，建立了滚动轴承复合故障的理论模型，并对滚动轴承复合故障的振动特性进行了分析。其次，提出了SONNR方法的四层结构：输入层，非凸稀疏正则化层，信号重构层和复合故障隔离层。最后，通过仿真数据和实际数据验证了该方法的有效性，并与传统的时域诊断方法和人工智能方法进行了比较。