Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method
Source abstract
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, 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Anhui University of Technology, Ma’anshan 243002, China
School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, China
Funding
Anhui Province Engineering Laboratory of Intelligent Demolition Equipment
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