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Article

Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method

1
Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Anhui University of Technology, Ma’anshan 243002, China
2
School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, China
*
Author to whom correspondence should be addressed.
Academic Editor: María Dolores Fernández Ramos
Coatings 2022, 12(6), 866; https://doi.org/10.3390/coatings12060866
Received: 20 May 2022 / Revised: 14 June 2022 / Accepted: 16 June 2022 / Published: 19 June 2022
To fully utilize the fault information and improve the diagnosis accuracy of rolling bearings, a multisensor feature fusion method is proposed. The method contains two steps. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition (VMD), and the redundant information such as noise is eliminated. Then, the time-domain, frequency-domain and multiscale entropy features are extracted based on the preferred IMF and fused into one multidomain feature dataset. In the second step, the deep autoencoder network (DAEN) is constructed and the multisensor fusion features of the first step are used as input of the DAEN, and the multisensor fusion features are further extracted and classified. The experimental results show that the proposed model has a higher classification accuracy compared with the existing methods. View Full-Text
Keywords: fault diagnosis; autoencoder network; multisensor; feature fusion; rolling bearing fault diagnosis; autoencoder network; multisensor; feature fusion; rolling bearing
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MDPI and ACS Style

Tong, J.; Liu, C.; Pan, H.; Zheng, J. Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method. Coatings 2022, 12, 866. https://doi.org/10.3390/coatings12060866

AMA Style

Tong J, Liu C, Pan H, Zheng J. Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method. Coatings. 2022; 12(6):866. https://doi.org/10.3390/coatings12060866

Chicago/Turabian Style

Tong, Jinyu, Cang Liu, Haiyang Pan, and Jinde Zheng. 2022. "Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method" Coatings 12, no. 6: 866. https://doi.org/10.3390/coatings12060866

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