Work

A Hybrid Fault Diagnosis Model for Rolling Bearing With Optimized VMD and Fuzzy Dispersion Entropy

Year: 2025

Type: article

Abstract: The vibration signal of rolling beating is nonlinear and nonstationary, which makes feature extraction difficult for fault diagnosis. To improve the efficiency of feature extraction and fault diagnosis, a hybrid model based on optimized variational mode decomposition (VMD), fuzzy dispersion entropy (FDE), and a support vector machine (SVM) is proposed. Firstly, a parameter optimization method using the sparrow search algorithm (SSA) was applied to VMD to improve the decomposition ability. Subsequently, a feature vector based on the FDE was proposed as a fault‐diagnosis feature. Finally, SVM was applied with the proposed feature vector for the fault diagnosis of rolling bearings. The simulation and experimental study results indicate that the proposed method can obtain useful features for fault diagnosis, particularly in short‐length samples and noise conditions. The proposed method performed well in the fault diagnosis for different fault types and degrees of rolling bearings.

Source: International Journal of Rotating Machinery

DOI: 10.1155/ijrm/7990867

Authors: Xin Xia, Xiaolu Wang, Weilin Chen

Institution: Suqian University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,000

Funder: National Natural Science Foundation of China