针对变分模态分解(VMD)在处理实际信号无法预先掌握其分解参数(K，α)而限制其使用，以及包含故障信息的特征参数的选取问题，提出了自适应变分模态分解(AVMD)算法。该算法首先以所分解模态的平均包络信息熵和包络峭度两种指标融合作为目标函数，利用蚱蜢算法(GOA)寻优，获取VMD的分解参数(K_op，α_op)，接着对原始振动信号进行VMD分解，通过能量百分比的计算，选取能量90%及以上的敏感模态，对其多域联合的特征参数构建特征向量，最后利用支持向量机(SVM)对滚动轴承的四种状态进行识别。通过滚动轴承数据集分析表明，采用AVMD方法提取的故障特征比EMD、EEMD、传统VMD以及PSO-VMD等方法提取的故障诊断特征的故障模式识别准确率更高，在测试数据集上的准确率达到99.166 7%。

For the problem that the decomposition parameters (K，α) of VMD could not be mastered in advance during actual signal processing and therefore its use was prohibited, as well as for the selection problem of the characteristic parameters containing fault information, an adaptive variational modal decomposition (AVMD) algorithm was proposed. In this algorithm, the average envelope entropy and envelope kurtosis of decomposed modes were fused as the objective function, and the decomposition parameters (K_op，α_op) of VMD were obtained by using the search and optimization of grasshopper algorithm (GOA). Then the original vibration signal was decomposed by VMD, the sensitive modes with 90% or more energy were selected through the calculation of energy percentage, and the feature vectors of their multidomain joint feature parameters were constructed. Finally, the support vector machine (SVM) was used to identify the four states of the rolling bearing. The rolling bearing data set analysis showed that the 99.166 7% accuracy of fault pattern recognition for fault diagnosis features extracted by AVMD method is higher than that extracted by EMD, EEMD, traditional VMD and PSO-VMD methods.