电子测量与仪器学报

谢锋云,孙恩广,宋明桦,宋成杰.多尺度韦伯色散熵图神经网络的齿轮箱复合故障诊断研究[J].电子测量与仪器学报,2025,39(9):244-253

多尺度韦伯色散熵图神经网络的齿轮箱复合故障诊断研究

Research on gearbox complex fault diagnosis based on multi-scaleWeibull dispersion entropy graph neural network

DOI：

英文关键词:gear box complex fault diagnosis multi-scale Weibull dispersion entropy graph neural network graph of feature

基金项目:国家自然科学基金（52265068）、江西省自然科学基金（20224BAB204050）项目资助

作者	单位
谢锋云	1.华东交通大学机电与车辆工程学院南昌330013；2.华东交通大学智能交通装备全寿命技术创新中心南昌330013
孙恩广	1.华东交通大学机电与车辆工程学院南昌330013；2.华东交通大学智能交通装备全寿命技术创新中心南昌330013
宋明桦	1.华东交通大学机电与车辆工程学院南昌330013；2.华东交通大学智能交通装备全寿命技术创新中心南昌330013
宋成杰	1.华东交通大学机电与车辆工程学院南昌330013；2.华东交通大学智能交通装备全寿命技术创新中心南昌330013

Author	Institution
Xie Fengyun	1.School of Mechanical Electronical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China; 2.China Lifecycle Technology Innovation Center of Intelligent Transportation Equipment, East China Jiaotong University, Nanchang 330013, China
Sun Enguang	1.School of Mechanical Electronical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China; 2.China Lifecycle Technology Innovation Center of Intelligent Transportation Equipment, East China Jiaotong University, Nanchang 330013, China
Song Minghua	1.School of Mechanical Electronical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China; 2.China Lifecycle Technology Innovation Center of Intelligent Transportation Equipment, East China Jiaotong University, Nanchang 330013, China
Song Chengjie	1.School of Mechanical Electronical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China; 2.China Lifecycle Technology Innovation Center of Intelligent Transportation Equipment, East China Jiaotong University, Nanchang 330013, China

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中文摘要:

齿轮箱是一种机械传动装置，针对齿轮箱复合故障信号非线性、不平稳导致状态识别效果不佳的问题，提出了一种基于多尺度韦伯色散熵图神经网络（WB MDEGNN）的齿轮箱复合故障诊断方法。首先，使用韦伯分布（Weibull distribution, WB）来线性化、平稳化振动信号，得到更加敏锐的齿轮箱状态信息，然后用多尺度色散熵（multi-scale dispersion entropy，MDE）提取给定序列的量化特征，并构建节点特征矩阵，其次使用K-近邻算法（k-nearest neighbor，KNN）提取节点特征的相关性，并构建边索引矩阵，将节点特征矩阵与边索引矩阵组合来构建特征图，最后将特征图输入到图神经网络（graph neural networks，GNN）模型，来进行分类识别。结果表明，通过压电式加速度传感器采集5种状态的齿轮箱数据，对采集的数据使用本文提出的WB-MDEGNN模型进行复合故障分类识别，相较于现有其他齿轮箱故障诊断方法正确率可提高6.07%~11.69%，同时通过向原始数据中添加不同信噪比的高斯白噪声和公开的数据集检验所提模型的准确度和泛化性，所提方法的复合故障诊断性能，准确度相差波动区间介于0.97%~3.38%，泛化性检验可达95%。因此，该方法在处理信号非线性、不平稳导致状态识别效果不佳的问题上具有较好的优越性，为齿轮箱的复合故障诊断提供了新的方法。

英文摘要:

A gearbox is a kind of mechanical transmission device. Aiming at the problem of poor state recognition effect caused by the nonlinearity and instability of the complex fault signal of the gearbox, a gearbox complex fault diagnosis method based on multi-scale Weibull dispersion entropy graph neural network (WB-MDEGNN) is proposed. Firstly, the Weibull distribution (WB) is used to linearize and stabilize the vibration signal to obtain more acute gearbox state information. Then, the Multi-scale dispersion entropy (MDE) is used to extract the quantization features of the given sequence. And construct the node feature matrix. Secondly, use the k-nearest neighbor (KNN) algorithm to extract the correlation of node features and construct the edge index matrix. Combine the node feature matrix with the edge index matrix to construct the feature map. Finally, the feature maps are input into the graph neural networks (GNN) model for classification and recognition. The results show that by collecting gearbox data in five states through piezoelectric acceleration sensors and using the WB-MDEGNN model proposed in this paper for complex fault classification and identification of the collected data, the accuracy rate can be increased by 6.07%~11.69% compared with other existing gearbox fault diagnosis methods. Meanwhile, the accuracy and generalization of the model proposed in this paper are tested by adding Gaussian white noise with different signal-to-noise ratios to the original data and public datasets. The complex fault diagnosis performance of the proposed method, the accuracy difference fluctuation range is between 0.97% and 3.38%, and the generalization test can reach 95%. Therefore, this method has better superiority in dealing with the problem of poor state recognition effect caused by signal nonlinearity and instability, providing a new method for the complex fault diagnosis of gearboxes.

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