A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM

Fuzheng Liu,Junwei Gao,Huabo Liu

doi:10.1109/access.2020.2998722

Abstract

The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately.

Highlights

Rolling bearings are important parts of rotating machinery equipment
EMD decomposition is performed on the obtained residual signals, and all the components are arranged from high frequency to low frequency. This method can suppress the modal aliasing phenomenon to a certain extent in the decomposition process, and reduces the amount of calculation, which is a modification on the EEMD algorithm and has huge advantages [14]. Due to these mentioned above, this paper adopts the modified ensemble empirical mode decomposition (MEEMD) algorithm to decompose the vibration signals to be detected adaptively, and the energy moment normalization is performed to obtain the feature vectors that can reflect the potential characteristics of the signals, which are used as the input for later fault diagnosis
The results show that the training time of the quantum particle swarm optimization (QPSO)-least squares support vector machine (LSSVM) model is 6.397s, while that of the particle swarm optimization (PSO)-LSSVM model is 10.982s

Summary

INTRODUCTION

Rolling bearings are important parts of rotating machinery equipment. Monitoring and diagnosing their internal latent faults timely and correctly are of great significance for ensuring their safe operation [1], [2]. F. Liu et al.: Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM algorithm which mainly adds two opposite white noise signals to the original signal to be analyzed, and performs EMD decomposition respectively. This method can suppress the modal aliasing phenomenon to a certain extent in the decomposition process, and reduces the amount of calculation, which is a modification on the EEMD algorithm and has huge advantages [14] Due to these mentioned above, this paper adopts the MEEMD algorithm to decompose the vibration signals to be detected adaptively, and the energy moment normalization is performed to obtain the feature vectors that can reflect the potential characteristics of the signals, which are used as the input for later fault diagnosis. Train and identify the fault types using the LSSVM model optimized by the QPSO algorithm

THE PRINCIPLE OF THE MEEMD ALGORITHM

THE PRINCIPLE OF THE LSSVM

Findings

CONCLUSION