Abstract
We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.
Highlights
Fusing refined composite multiscale dispersion entropy (RCMDE) and Sparrow SearchRolling bearings are widely used in transmissions, generators, machine tools, and other high-speed rotating machinery
The results show that the fault-recognition accuracy of the salp swarm optimization support vector machine (SSO-SVM) in reference [20] reaches 100%, which is superior to particle swarm optimization (PSO) and the grey wolf optimizer (GWO)
This paper proposes a rolling-bearing-fault diagnosis model based on variational mode decomposition (VMD), RCMDE, and sparrow search algorithm (SSA)-optimized SVM
Summary
Fusing RCMDE and Sparrow SearchRolling bearings are widely used in transmissions, generators, machine tools, and other high-speed rotating machinery. The performance of the rotating mechanism will be affected [1]. The operational environment of the bearing is complex, and various types of faults can appear in the long-term work, causing potential danger to the mechanical operations [2]. In the field of bearing fault diagnosis, many similar algorithms have the disadvantages of low classification accuracy and long calculation time. These shortcomings lead to the poor effect of fault diagnosis in practical application. How to identify fault types quickly and effectively has become an important topic. This paper proposes a new bearing-fault diagnosis method, which integrates several algorithms to help quickly detect bearing faults and reduce losses
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