*Article* **Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method**

**Jinyu Tong 1,2, Cang Liu 2, Haiyang Pan 2 and Jinde Zheng 2,\***


**Abstract:** 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, frequencydomain 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.

**Keywords:** fault diagnosis; autoencoder network; multisensor; feature fusion; rolling bearing

#### **Citation:** 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

 Academic Editor: María Dolores Fernández Ramos

Received: 20 May 2022 Accepted: 16 June 2022 Published: 19 June 2022

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