基于 LCD 多尺度融合特征 GJO-SVM 的电机轴承故障诊断
DOI:
作者:
作者单位:

作者简介:

通讯作者:

基金项目:


Motor Bearing Fault Diagnosis Based on LCD Multi-scale Fusion Features GJO-SVM
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    目的 针对电机滚动轴承故障难以提取而导致诊断准确率低问题,提出了一种局部特征尺度分解( Local Characteristic-scale Decomposition, LCD) 多尺度融合的特征提取方法,并将提取得到的特征向量输入到采用金豺算 法( Golden Jackal Optimization, GJO) 优化后的支持向量机( Support Vector Machine, SVM) 里进行电机轴承故障诊 断,从而提升故障诊断正确率。 方法 首先,使用 LCD 算法对使用振动传感器采集得到的电机轴承信号进行分解, 得到信号在前 3 个尺度下的内禀尺度分量( Intrinsic Scale Components, ISC) ;其次,求每个 ISC 的能量比和能量熵 得到 6 组特征向量;然后,构建特征向量矩阵并按照 3 ∶ 2 随机选取,生成训练集和测试集;最后,把训练集输入到 金豺算法优化支持向量机( GJO-SVM) 中,采用 GJO 算法对支持向量机中的惩罚参数 C 和核参数 g 进行寻优选择 建立出 GJO-SVM 故障诊断模型,接着将测试集输入到模型中以实现轴承的故障识别。 结果 通过仿真实验验证, LCD 多尺度融合特征的特征提取方法能够有效地提取电机轴承的故障信息,且使用金豺算法优化支持向量机 ( GJO- SVM ) 的 故 障 诊 断 准 确 率 达 97. 86%。 结 论 在 同 样 的 条 件 下 与 变 分 模 态 分 解 ( Variational Modal Decomposition,VMD) 融合特征相比,LCD 多尺度融合特征的诊断准确率提升了 1. 79%;与粒子群算法优化支持向 量机( PSO-SVM) 方法、果蝇算法优化支持向量机( FOA-SVM) 方法相比,金豺算法优化支持向量机( GJO-SVM) 诊 断电机轴承故障的方法具有更高的故障识别效果。

    Abstract:

    Aiming at the problem of low diagnostic accuracy caused by difficult extraction of motor rolling bearing faults a feature extraction method of local characteristic-scale decomposition LCD multi-scale fusion was proposed. The extracted feature vectors were input into the support vector machine SVM optimized by golden jackal optimization GJO for motor bearing fault diagnosis so as to improve the accuracy of fault diagnosis. Methods Firstly the LCD algorithm was used to decompose motor bearing signals collected by the vibration sensor to obtain intrinsic scale components ISC of the signal at the first three scales. Secondly the energy ratio and energy entropy of each ISC were calculated to obtain 6 sets of feature vectors. Then the feature vector matrix was constructed and randomly selected according to 3 ∶ 2 to generate a training set and test set. Finally the training set was input into the golden jackal algorithm optimization support vector machine GJO-SVM and the GJO algorithm was used to optimize the penalty parameter C and kernel parameter g in the support vector machine to establish the GJO-SVM fault diagnosis model. The test set was input into the model to realize the fault identification of the bearing. Results Through simulation experiments it is verified that the feature extraction method of LCD multi-scale fusion features can effectively extract the fault information of motor bearings and the fault diagnosis accuracy of GJO-SVM is 97. 86%. Conclusion Under the same conditions the diagnostic accuracy of the LCD multi-scale fusion feature is improved by 1. 79% compared with the fusion feature of variational modal decomposition VMD . Compared with the particle swarm optimization-support vector machine PSO- SVM method and the fruit fly optimization algorithm-support vector machine FOA-SVM method the GJO-SVM method has a higher fault identification effect for diagnosing motor bearing faults.

    参考文献
    相似文献
    引证文献
引用本文

王磊,刘国龙,田辉,王志强,冯萌,杨磊,姚学龙,包 桦,马向阳.基于 LCD 多尺度融合特征 GJO-SVM 的电机轴承故障诊断[J].重庆工商大学学报(自然科学版),2024,41(5):1-8
WANG Lei LIU Guolong TIAN Hui WANG Zhiqiang FENG Meng YANG Lei YAO Xuelong BAO Hua MA Xiangyang. Motor Bearing Fault Diagnosis Based on LCD Multi-scale Fusion Features GJO-SVM[J]. Journal of Chongqing Technology and Business University(Natural Science Edition),2024,41(5):1-8

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-09-29
×
2023年《重庆工商大学学报(自然科学版)》影响因子稳步提升