We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

中文翻译：

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融合RCMDE和Sparrow搜索算法的基于SVM的滚动轴承故障诊断协调方法

我们通过集成变分模式分解 (VMD)、精细复合多尺度色散熵 (RCMDE) 和通过麻雀搜索算法 (SSA) 优化的支持向量机 (SVM)，提出了一种滚动轴承的新型故障诊断方法。首先从各种信号分解方法中选择VMD对原始信号进行分解。然后，通过RCMDE提取信号特征作为诊断模型的输入。与多尺度样本熵（MSE）和多尺度色散熵（MDE）相比，RCMDE被证明是优越的。之后，利用 SSA 搜索 SVM 的最优参数来识别不同的故障。最后，通过风力涡轮机传动系统诊断模拟器 (WTDS) 收集的实验数据验证和评估了所提出的协调 VMD-RCMDE-SSA-SVM 方法。