Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

Long, Han; Li, Chengwei; Liu, Hongchen

doi:10.3390/e17106683

Cited by 14 publications

(10 citation statements)

References 12 publications

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“…Later, Meng, Lei, and Kong () used the cloud model to solve the uncertainty problem which existed in the artificial immune diagnosis method. Besides, Han, Li, and Liu () used the cloud model characteristic entropy to represent the characteristic information of the fault signal. Liu, Sun, Chen, Sun, and Li () used the cloud model to evaluate the sensory characteristics of five kinds of corn beverages, and found that the cloud model could distinguish the beverages well.…”

Section: Introductionmentioning

confidence: 99%

A comparison of food crispness based on the cloud model

Wang

Sun

Hou

et al. 2017

Journal of Texture Studies

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Section: Introductionmentioning

confidence: 99%

A comparison of food crispness based on the cloud model

Wang

Sun

Hou

et al. 2017

Journal of Texture Studies

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show abstract

“…To solve the mode mixing problem exists in EMD, which would result in IMF distortion, EEMD was proposed [ 19 , 20 , 21 ]. The EEMD algorithm is a noise-assisted signal processing method, and EEMD performs EMD multiple times on the signal superimposed with Gaussian white noise.…”

Section: Methodsmentioning

confidence: 99%

“…Compared to other signal processing methods in the field of mechanical fault diagnosis, EMD is an adaptive nonlinear and nonstationary signal processing method, and has no requirement of basic functions. However, EMD still has modal aliasing and end effect problems, and the ensemble empirical mode decomposition (EEMD) [ 19 , 20 , 21 ] was proposed to alleviate the mode aliasing problem by utilizing the property of frequency uniform distribution of Gaussian white noise. Then complementary ensemble empirical mode decomposition (CEEMD) [ 22 ] was proposed to improve EEMD by adding positive and negative Gaussian white noise.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Wang

et al. 2018

Materials

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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.

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“…(1) e extreme point continuation method [9]: the basic principle of this method involves extending the original signal data to a certain number of extreme points. One of the main advantages of this algorithm is that it is simple and easy to implement.…”

Section: Adaptive Extreme Point-matching Continuationmentioning

confidence: 99%

Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges

Wen

Khan

et al. 2020

Shock and Vibration

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Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters.

show abstract

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

Cited by 14 publications

References 12 publications

A comparison of food crispness based on the cloud model

A comparison of food crispness based on the cloud model

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

Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges

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