Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

Abstract

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition

“…Li applied EMD on railway wheel flat detection [15]. Li and Abdelkader applied EMD to diagnose rolling bearing faults, respectively [16,17]. Fang applied EMD to forecast agricultural product futures prices and Willard applied EMD to analysis of rainfall and temperature data [18,19].…”

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

“…This algorithm showed unique advantages in detecting the impact characteristics of signals and effectively extracted the fault features of low-speed bearings. Li et al [ 16 ] used the improved complete CEEMD with adaptive noise method to decompose bearing vibration signals, extracted nonlinear entropy features, and built a multiclass intelligent recognition model based on an integrated support vector machine, effectively classifying experimental data under various operating conditions. Variational mode decomposition (VMD) is a kind of adaptive signal decomposition method.…”

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing

“…As one of the most intelligent and cutting-edge fields in the field of artificial intelligence, the application of SVM received increasing attention [ 12 , 13 , 14 ], reflecting in the aspects of regression estimation, pattern recognition, and fault diagnosis, such as the fault diagnosis of the vehicle suspensions, automatic detection of diabetic eye disease, and predictive control of the industrial process [ 15 , 16 , 17 ]. In the aspect of bearing fault diagnosis, the application of SVM has been reported in many literatures [ 18 , 19 , 20 , 21 ]. For example, Gu et al [ 18 ] proposes an approach based on the variational mode decomposition, support vector machine, and statistical characteristics to analyze the vibration signals of bearing on the spindle device of the mine hoist.…”

“…For example, Gu et al [ 18 ] proposes an approach based on the variational mode decomposition, support vector machine, and statistical characteristics to analyze the vibration signals of bearing on the spindle device of the mine hoist. Li et al [ 19 ] used ensemble SVM for the intelligent classification of the bearing’s faults, combined with the nonlinear dynamics entropy. Van et al [ 20 ] proposed a hybrid fault-diagnosis method for bearing based on the particle swarm optimization and least squares wavelet support vector machine, whose feature vectors are obtained by minimum-redundancy maximum-relevance method.…”