Heterogeneous Network Bearing Fault Diagnosis Method Based on Multi-Sensor Data Fusion

被引：0

作者：

Zhao, Xiaoqiang ^{[1
,2
,3
]}

Li, Sen ^{[1
]}

机构：

[1] School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou

[2] Gansu Provincial Key Laboratory of Advanced Industrial Process Control, Lanzhou University of Technology, Lanzhou

[3] National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou

来源：

Computer Engineering and Applications | 2025年 / 61卷 / 05期

关键词：

complementary features; data fusion; fault diagnosis; heterogeneous networks; multi-sensor; rolling bearing;

D O I：

10.3778/j.issn.1002-8331.2406-0294

中图分类号：

学科分类号：

摘要：

To address the problems that the input of a single-sensor and a single-branch network is easily affected by external interference and that the loss of feature information during the conversion of signals from different domains leads to poor fault diagnosis, this paper proposes a fault diagnosis method for reciprocal network bearings based on multi-sensor data fusion. Firstly, the method designs a data preprocessing module to achieve complementary fault features from multiple sensors with multi-angle fault features by data-level fusion, which fully takes into account the correlation between multi-sensors of bearing equipment. At the same time, the signals processed by fast Fourier transform (FFT) and frequency sliced wavelet transform (FSWT) are fused into multi-domain signals as inputs to the model. Using independent multi-domain signals as model inputs ensures that the key feature information of different domain signals will not be lost during the conversion process. Secondly, the method designs a mutually exclusive network structure for different domain signals to extract lowdimensional features in the high-dimensional nonlinear space of multi-sensor data, which also provides a more reliable and convenient means of maintenance for equipment maintenance personnel. When the input of one of the branch networks is subjected to external interference, the other two branch networks will play the role of error correction, which not only enhances the fault tolerance of the network, but also increases the feature complementary ability of the network. Further, the features are regarded as different time steps by using the memory unit, which establishes the dependency relationship between different fault features. Finally, in order to prevent the model from falling into a local optimum, model training is optimized using a learning rate cosine annealing algorithm adapted to the proposed model. Experiments are conducted on two bearing datasets, and the results show that the method in this paper possesses good fault diagnosis results and generalization ability, and can meet the task of bearing fault diagnosis based on multi-sensor data fusion. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：323 / 333

页数：10

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