A novel fault diagnosis method is proposed for rolling bearing by combining extreme-point symmetric mode decomposition (ESMD) composite multiscale weighted permutation entropy (CMWPE) and gravitational search algorithm based on multiple adaptive constraint strategy (MACGSA) optimized least squares support vector machine (LSSVM). In order to solve the problem of intrinsic mode function (IMF) modal aliasing and small differences in fault features, ESMD and CMWPE are used to obtain a more sensitive high-dimensional feature vector set. Aiming at the low accuracy of LSSVM fault diagnosis, MACGSA was used to optimize LSSVM to improve the accuracy of fault diagnosis. ESMD is used to process the rolling bearing data to obtain a series of IMFs; Then, extracting the CMWPE values of IMFs to form a high-dimensional feature vector set; Finally, the MACGSA-LSSVM model is adopted to achieve fault classification. Compared with other diagnostic methods, this method has higher diagnostic accuracy.

结合极端点对称模式分解（ESMD）复合多尺度加权置换熵（CMWPE）和基于多重自适应约束策略（MACGSA）优化最小二乘支持向量机（LSSVM）的重力搜索算法，提出了一种新型的滚动轴承故障诊断方法。 ）。为了解决固有模式函数（IMF）模态混叠和故障特征差异小的问题，ESMD和CMWPE用于获得更敏感的高维特征向量集。针对LSSVM故障诊断的准确性低的问题，采用MACGSA对LSSVM进行优化，以提高故障诊断的准确性。ESMD用于处理滚动轴承数据以获得一系列IMF。然后，提取IMF的CMWPE值以形成高维特征向量集；最后，采用MACGSA-LSSVM模型进行故障分类。与其他诊断方法相比，该方法具有更高的诊断准确性。