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基于可视图谱特征融合的行星齿轮箱故障诊断
作者:
作者单位:

1.北京信息科技大学机电工程学院 北京 100192; 2.北京信息科技大学现代测控教育部重点实验室 北京 100192

中图分类号:

TH132.425;TN06

基金项目:

国家自然科学基金(62303065)项目资助


Fault diagnosis of planetary gearbox based on visual spectral feature fusion
Author:
Affiliation:

1.College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University，Beijing 100192, China; 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University，Beijing 100192, China

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    摘要:

    针对行星齿轮箱振动信号频率信息复杂、时变性强、调制特征明显的问题，提出了基于可视图谱特征融合的行星齿轮箱故障诊断方法。首先将行星齿轮箱信号进行Welch变换得到功率谱，采取可视图算法构建图谱，计算图谱节点的中心性指标并融合成特征矩阵，最后使用改进的CNN-Inception模型分类得到齿轮箱故障诊断结果。实验结果表明，该方法可以准确识别行星齿轮箱故障，在两种工况的实验数据集上准确率可以达到98.57%，模型具有泛化性。相较于其他方法，该方法能够实现高效、准确的故障诊断。

    Abstract:

    To solve the problems of complex frequency information, strong time variation and obvious modulation characteristics of planetary gearbox vibration signal, a fault diagnosis method of planetary gearbox based on visual spectral feature fusion was proposed. Initially, Welch′s transformation is applied to planetary gearbox signals to obtain power spectra. Subsequently, a visual graph algorithm is used to construct a graph spectrum, and centrality measures of the graph nodes are calculated to form a feature matrix. Finally, an improved CNN-Inception model is employed to obtain the fault diagnosis results of the planetary gearbox. Experimental results demonstrate that this method can accurately identify faults in planetary gearboxes. In the experimental datasets covering two operational conditions, the model achieves an accuracy of 98.57%, demonstrating its generalization ability. Compared with alternative methods, the proposed approach exhibits higher accuracy and stronger generalization capabilities.

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荆新岚,黄民,马超.基于可视图谱特征融合的行星齿轮箱故障诊断[J].电子测量技术,2024,47(24):171-178

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  • 在线发布日期: 2025-01-24
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