基于卷积胶囊网络的行星齿轮箱故障诊断方法
作者:
作者单位:

1.天津理工大学;2.电子科技大学

作者简介:

通讯作者:

中图分类号:

TH17

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Fault Diagnosis Method of Planetary Gearbox Based on Convolutional Capsule Network
Author:
Affiliation:

1.Tianjin University of Technology;2.University of Electronic Science and Technology of China

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    行星齿轮箱是大型机械装备的核心传动环节,其内部关键零部件故障发生部位与形式多样,威胁机械装备的安全服役.针对其不同部件的多类故障诊断问题,提出了一种基于改进的胶囊网络(Enhanced Capsule Network, (ECN))的“端到端”智能故障诊断方法. ECN既充分发挥了卷积神经网络对故障特征的深度提取能力,同时具备胶囊结构矢量化挖掘特征空间信息的特点,可利用动态路由机制计算胶囊层之间的相关度,从而实现了对故障特征的精准归类.此外,通过建立的间隔损失函数与输入数据不断优化模型参数,实现了对故障的智能诊断.通过对太阳轮、行星轮以及多类部件故障数据混合的条件下分别开展分析,实验结果表明, ECN相比传统卷积神经网络与胶囊网络均表现出更强的故障诊断能力.

    Abstract:

    Planetary gearboxes (PGs) are the core transmission link of large-scale mechanical equipment. The fault of key components occurring inside a PG usually exhibits in various locations and types, therefore, threatens the security service of mechanical equipment. To this point, an end-to-end intelligent diagnosis strategy based on Enhanced Capsule Network (ECN) is proposed to detect multiple fault types of different components in a PG. The ECN well merits the advantages of the Convolution Neural Network (CNN) and the Capsule Network (CN), which fully retains spatial information while digging in-depth fault features. The dynamic routing algorithm is applied to calculate the correlation between the different capsule layers so that accurately recognizing the fault feature. Furthermore, ECN continuously optimizes model parameters by the established margin loss functions and the input data. The experimental studies are conducted to demonstrate the effectiveness of the proposed model, which including two scenarios: 1. different faults from one component; 2. mixing the multiple faults of different components. The experimental results show that ECN featured stronger fault diagnosis capabilities than traditional CNN and CN.

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历史
  • 收稿日期:2021-08-16
  • 最后修改日期:2021-11-29
  • 录用日期:2021-12-09
  • 在线发布日期: 2022-01-02
  • 出版日期: