Fault identification for bearings of special electromechanical equipment is significant to avoid catastrophic accidents. However, spectral aliasing and nonstationarity resulted from variable speed condition make this task difficult. In this paper, a modified self-organizing maps (SOM) based on nonlinear neural weight updating way is proposed to solve the problem of bearing fault severity identification in variable speed condition. Firstly, a multi-domain features extraction method based on angular re-sampling technique is introduced. Then considering the nonlinear relationship between fault severity and fault features, the traditional Euclidian distance of SOM is substituted with the geodesic distance when update the neural weight and select the best-matching cell, which can improve the nonlinear identification ability of proposed method. Finally, two cases are performed and the results show that the method can identify bearing fault with different severities effectively and have practical significance when considering both accuracy and time cost compared with other methods.

特殊机电设备轴承的故障识别对于避免灾难性事故很重要。但是，变速条件导致的频谱混叠和非平稳性使此任务变得困难。提出了一种基于非线性神经权重更新的改进的自组织映射图（SOM），以解决变速条件下轴承故障严重性识别的问题。首先，介绍了一种基于角度重采样技术的多域特征提取方法。然后考虑故障严重程度与故障特征之间的非线性关系，在更新神经元权重并选择最佳匹配单元时，用测地距离代替了传统的SOM欧氏距离，从而提高了该方法的非线性识别能力。最后，