基于图卷积和多传感融合的跨设备故障诊断方法

孙元帅; 孔繁钦; 聂晓音; 谢刚

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基于图卷积和多传感融合的跨设备故障诊断方法

更新时间：2025-01-03

- 基于图卷积和多传感融合的跨设备故障诊断方法
- Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion
- 机械强度 2024年页码：1-10
- 作者机构：
  
  1.太原科技大学电子信息工程学院，太原 030024
  2.先进控制与工业智能山西省重点实验室，太原 030024
- 作者简介：
  
  孙元帅，男，1998年生，山东潍坊人，太原科技大学在读硕士研究生；主要研究方向为故障诊断；E-mail：810591471@qq.com。
  谢刚（通信作者），男，1972年生，山西太原人，博士，教授；主要研究方向为机器视觉与图像处理、物联网安全与隐私、大数据驱动的智能故障诊断；E-mail：xiegang@tyust.edu.cn。
- 基金信息：
  
  山西省科技重大专项计划“揭榜挂帅”项目(202201090301013);山西省重点研发计划项目(202102020101002;202202100401002;202202150401005);山西省基础研究计划（自由探索类）面上项目(20210302123206;202203021211194;202203021221142;202203021222186)
- DOI：
  中图分类号： TP277
- 网络出版日期：2024-12-31，
  
  收稿日期：2024-05-05，
  
  修回日期：2024-06-07，
- 稿件说明：
移动端阅览
孙元帅，孔繁钦，聂晓音等.基于图卷积和多传感融合的跨设备故障诊断方法［J］.机械强度，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001.

SUN Yuanshuai，KONG Fanqin，NIE Xiaoyin，et al.Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion［J］.Journal of Mechanical Strength，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001.
孙元帅，孔繁钦，聂晓音等.基于图卷积和多传感融合的跨设备故障诊断方法［J］.机械强度，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001. DOI：

SUN Yuanshuai，KONG Fanqin，NIE Xiaoyin，et al.Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion［J］.Journal of Mechanical Strength，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001. DOI：

摘要

目的

对于实际生产中的机械设备，很难或无法获取大量的标记数据，导致传统故障诊断方法的准确率较低。针对此问题，提出一种基于图卷积和多传感融合的跨设备故障诊断方法（Convolutional Domain Graph Convolution Network， CDGCN）。该方法可以对类标签、域标签和数据特征结构进行建模。

方法

首先，使用卷积神经网络从输入信号中提取特征，然后，通过图生成层挖掘样本的特征结构关系来构建实例图。利用图卷积神经网络对实例图进行建模，同时提出多传感高层特征融合方式，进行多传感器的信息融合。最后，利用分布差异度量、分类器和域判别器实现域自适应。

结果

所提方法可以捕获域不变特征和判别特征，实现跨设备的故障诊断。通过两个数据集的迁移试验表明，所提出的CDGCN不仅在比较方法中获得了最佳性能，而且能够提取可转移的特征进行跨设备的域自适应。

Abstract

Objective

For mechanical equipment in actual production

it is difficult or impossible to obtain a large amount of labeled data

resulting in low accuracy of traditional fault diagnosis methods. To address this problem

a cross-device fault diagnosis method based on graph convolution and multi-sensor fusion

convolutional domain graph convolution network (CDGCN)

was proposed. This method can model class labels

domain labels

and data feature structures.

Methods

Firstly

a convolutional neural network was used to extract features from the input signal. Then

the feature structure relationship of the sample was mined through the graph generation layer to construct an instance graph. The instance graph was modeled using a graph convolutional neural network

and a multi-sensor high-level feature fusion method was proposed to perform multi-sensor information fusion. Finally

domain adaptation was achieved by using distribution difference metrics

classifiers

and domain discriminators.

Results

The proposed method can capture domain-invariant features and discriminant features

and ultimately achieve cross-device fault diagnosis. Migration experiments on two datasets show that the proposed CDGCN not only achieves the best performance among the compared methods

but also extracts transferable features for cross-device domain adaptation.

关键词

图卷积神经网络多传感器跨设备域自适应故障诊断

Keywords

Graph convolutional neural networkMulti-sensorCross-deviceDomain adaptationFault diagnosis

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