The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods. This paper focuses on the sensitive features extraction and pattern recognition for rolling bearing fault diagnosis and proposes a novel intelligent fault-diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM). Firstly, a novel non-linear technology named GCMWPE was presented, allowing the extraction of bearing features from multiple scales and enabling the construction of a high-dimensional feature set. The GCMWPE uses the generalized composite coarse-grained structure to overcome the shortcomings of the original structure in multiscale weighted permutation entropy and obtain more stable entropy values. Subsequently, the S-Iso algorithm was introduced to obtain the main features and reduce the GCMWPE set dimensionality. Finally, a combination of GCMWPE and S-Iso set was input to the MPA-SVM for diagnosis and identification. The marine predators algorithm (MPA) was used to obtain the optimal SVM parameters. The effectiveness of the proposed fault diagnosis method was confirmed through two bearing fault diagnosis experiments. The results have shown that the proposed method can be used to correctly diagnose bearing states with high diagnostic accuracy.

滚动轴承振动信号复杂、非线性、非平稳，常规信号处理方法难以提取敏感特征和诊断故障。本文重点研究滚动轴承故障诊断的敏感特征提取和模式识别，提出了一种基于广义复合多尺度加权排列熵（GCMWPE）、监督Isomap（S-Iso）和海洋捕食者算法的新型智能故障诊断方法——基于支持向量机 (MPA-SVM)。首先，提出了一种名为 GCMWPE 的新型非线性技术，允许从多个尺度提取轴承特征并构建高维特征集。GCMWPE采用广义复合粗粒度结构，克服了原有结构在多尺度加权置换熵上的缺点，获得更稳定的熵值。随后，引入S-Iso算法获取主要特征并降低GCMWPE集维数。最后，将 GCMWPE 和 S-Iso 集的组合输入 MPA-SVM 进行诊断和识别。海洋捕食者算法（MPA）用于获得最优SVM参数。通过两次轴承故障诊断实验验证了所提出的故障诊断方法的有效性。结果表明，所提出的方法可以正确诊断轴承状态，具有较高的诊断精度。随后，引入S-Iso算法获取主要特征并降低GCMWPE集维数。最后，将 GCMWPE 和 S-Iso 集的组合输入 MPA-SVM 进行诊断和识别。海洋捕食者算法（MPA）用于获得最优SVM参数。通过两次轴承故障诊断实验验证了所提出的故障诊断方法的有效性。结果表明，所提出的方法可以正确诊断轴承状态，具有较高的诊断精度。随后，引入S-Iso算法获取主要特征并降低GCMWPE集维数。最后，将 GCMWPE 和 S-Iso 集的组合输入 MPA-SVM 进行诊断和识别。海洋捕食者算法（MPA）用于获得最优SVM参数。通过两次轴承故障诊断实验验证了所提出的故障诊断方法的有效性。结果表明，所提出的方法可以正确诊断轴承状态，具有较高的诊断精度。通过两次轴承故障诊断实验验证了所提出的故障诊断方法的有效性。结果表明，所提出的方法可以正确诊断轴承状态，具有较高的诊断精度。通过两次轴承故障诊断实验验证了所提出的故障诊断方法的有效性。结果表明，所提出的方法可以正确诊断轴承状态，具有较高的诊断精度。