Hybrid Fault Diagnosis of Mechanical Rotating Parts Based on Double Channel Information Fusion

Home > Archive>Volume 44, Issue 14, 2021 >77-83

Hybrid Fault Diagnosis of Mechanical Rotating Parts Based on Double Channel Information Fusion
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                        Wang TingxuanWang Tingxuan
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Liu TaoLiu Tao
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Wang ZhenyaWang Zhenya
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Yang YongcanYang Yongcan
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Affiliation:1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China; 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
Clc Number:TN98; TN06; TH165. 3
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Abstract:

In order to achieve high accuracy in hybrid fault diagnosis of rotating parts, a hybrid fault diagnosis method based on information fusion of two channels under variable working conditions is proposed in this paper. The signal includes both rolling bearing and gear vibration signal. The vibration signal of channel 1 is generated by generalized S transform, and the feature map is used as the model input of channel 1. In channel 2, the time-domain signals of the rotating parts are taken as the characteristic input, and the two-channel output layer is randomly fused with the features. By fine-tuning the parameters of the whole two-channel convolutional neural network (CNN) model, the diagnosis and identification of the mixed fault state of the rotating parts under varying working conditions are realized. The results show that the proposed method can be effectively applied to the mixed fault identification and diagnosis of rotating parts. Compared with the one-dimensional and two-dimensional convolutional neural network and other machine learning methods, the proposed method has the highest fault identification accuracy, reaching 98.18%.

Key words:Rotating parts; Convolutional neural network; Generalized S-transform; Hybrid fault diagnosis

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Online: September 05,2024
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