A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition.

Weibo Zhang,Jianzhong Zhou

doi:10.3390/e21070680

Abstract

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

Highlights

Rotating machinery is a major part of mechanical equipment, including many engineering fields such as power, chemical, metallurgy, and machinery manufacturing [1,2,3,4]
The vibration data of each working condition was divided into 110 non-overlapped samples, and each sample consisted of 1024 data points
To agree with the actual engineering application, 20 percent of each working condition sample was randomly selected for training, and the remains were used as test samples to validate the effectiveness of the presented method

Summary

Introduction

Rotating machinery is a major part of mechanical equipment, including many engineering fields such as power, chemical, metallurgy, and machinery manufacturing [1,2,3,4]. Its working condition directly affects the safety and stability of mechanical operation. Bearings are one of the most common and fragile general parts in rotating machinery, and their health is directly related to whether the machine can operate normally. It is necessary to execute health monitoring and fault diagnosis of the bearing, and it has drawn considerable attention and research. As bearings operate, they unavoidably suffer from cracks, corrosion, spalling and other factors, which cause the vibration signals to exhibit nonlinear dynamic characteristic. How to effectively extract and detect fault characteristics of bearings is crucial in fault diagnosis [5]

Methods

Results

Conclusion