The long-term safe operation of rotating machinery is closely related to the stability of rolling bearings. This paper proposes a rolling bearing fault diagnosis method based on refined composite multiscale fuzzy entropy (RCMFE), topology learning and out-of-sample embedding (TLOE), and the marine predators algorithm based-support vector machine (MPA-SVM). First, the RCMFE algorithm is used to extract the features of the original rolling bearing fault signal and to construct the original high-dimensional fault feature set. Then, TLOE is used to reduce the dimensionality of the high-dimensional fault feature set. The low-dimensional sensitive fault features are extracted to construct a low-dimensional fault feature subset. Finally, fault-type discrimination is performed using the MPA-SVM. The Case Western Reserve University dataset and data from fault diagnosis experiments performed on 1210 self-aligning ball bearings were used to verify the proposed method. The results demonstrate the effectiveness of the fault diagnosis method, which can diagnose bearing faults with up to 100% accuracy.

旋转机械的长期安全运行与滚动轴承的稳定性密切相关。提出了一种基于精细复合多尺度模糊熵（RCMFE），拓扑学习和样本外嵌入（TLOE）以及基于海洋捕食者算法的支持向量机（MPA-SVM）的滚动轴承故障诊断方法。首先，使用RCMFE算法提取原始滚动轴承故障信号的特征，并构建原始的高维故障特征集。然后，使用TLOE减少高维故障特征集的维数。提取低维故障特征，以构造低维故障特征子集。最后，使用MPA-SVM执行故障类型判别。凯斯西储大学的数据集和对1210个自动调心球轴承进行故障诊断实验的数据被用来验证该方法。结果证明了故障诊断方法的有效性，该方法可以以高达100％的精度诊断轴承故障。