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基于自适应Morlet小波的牵引电机故障识别研究
许文梦 2026/3/10 18:04:58
泰山科技学院，智能工程学院，山东泰安 271000
摘要：为了提高轨道交通牵引传动电机早期故障诊断能力，开发了一种Morlet小波自适应参数字典算法，实现局部分割并整体数据进行全局分析的功能，通过鲸鱼优化算法(WOA)自主计算小波字典数据。根据正交匹配追踪(OMP)结果对振动信号开展稀疏分解，以包络谱方法获取电机中的早期信号，实现了电机的高效故障诊断。仿真结果表明：引入背景噪声的情况下故障特征变得模糊，相比于CFA等其他方法，本文方法对分辨力和运行时间的影响更显著。试验结果表明：从包络谱中看到明显电机故障频率及谐波，表明此时已经能够准确识别出故障。该研究可以拓展到其它的机械传动系统上，具有很高的推广价值。
关键词：电机；故障诊断；稀疏表示；Morlet小波；鲸鱼优化算法
中图分类号：TH212
Research on Fault Identification of Traction Motor Based on Adaptive Morlet Wavelet
Xu Wenmeng
Taishan University of Science and Technology, School of Intelligent Engineering, Tai ’an, Shandong 271000
Abstract: In order to improve the early fault diagnosis ability of traction drive motors in rail transit, a Morlet wavelet adaptive parameter dictionary algorithm has been developed to achieve the function of local segmentation and global analysis of the overall data, and the wavelet dictionary data is autonomously calculated through the Whale Optimization Algorithm (WOA). Based on the results of Orthogonal Matching Pursuit (OMP), the vibration signals were subjected to sparse decomposition, and the early signals in the motor were obtained by the envelope spectrum method, achieving efficient fault diagnosis of the motor. The simulation results show that the fault characteristics become blurred when background noise is introduced. Compared with other methods such as CFA, the method proposed in this paper has a more significant impact on resolution and running time. The test results show that obvious motor fault frequencies and harmonics can be seen from the envelope spectrum, indicating that the fault can be accurately identified at this time. This research can be extended to other mechanical transmission systems and has high promotion value.
Key words: Motor Fault diagnosis Sparse representation Morlet Xiaobo Whale optimization algorithm
0 引言
电机在当前牵引传动系统中获得了广泛应用，对于传递牵引载荷起到了关键作用[1]。对于实际工程控制过程，需要可靠地进行电机缺陷识别和定位，这是保证设备正常运行的重要基础[2]。当机械机构受到高频负载影响（未完，下一页）
附件下载：基于自适应Morlet小波的牵引电机故障识别研究
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