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Open AccessArticle

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

by Yong Lv 1,2, Rui Yuan 1,2,*, Tao Wang 1,2, Hewenxuan Li 3 and Gangbing Song 4
1
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
3
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA
4
Smart Material and Structure Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA
*
Author to whom correspondence should be addressed.
Materials 2018, 11(6), 1009; https://doi.org/10.3390/ma11061009
Received: 16 May 2018 / Revised: 6 June 2018 / Accepted: 11 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Deformation, Fatigue and Fracture of Materials)
Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings. View Full-Text
Keywords: health degradation monitoring; early fault diagnosis; CEEMDAN; improved MMSE; smoothed coarse graining process; rolling bearing health degradation monitoring; early fault diagnosis; CEEMDAN; improved MMSE; smoothed coarse graining process; rolling bearing
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Lv, Y.; Yuan, R.; Wang, T.; Li, H.; Song, G. Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE. Materials 2018, 11, 1009.

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