CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed

Bouhalais, Mohamed Lamine; Djebala, Abderrazek; Ouelaa, Nouredine; Babouri, Mohamed Khemissi

doi:10.1007/s00170-017-1044-0

Cited by 37 publications

(11 citation statements)

References 26 publications

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“…In CEEMDAN decomposition, adaptive white noise is introduced into each residual component [ 33 ]. The algorithm procedure will briefly be discussed in the following steps.…”

Section: Review Of Ceemdanmentioning

confidence: 99%

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Shang

et al. 2020

Entropy

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To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

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“…In CEEMDAN decomposition, adaptive white noise is introduced into each residual component [ 33 ]. The algorithm procedure will briefly be discussed in the following steps.…”

Section: Review Of Ceemdanmentioning

confidence: 99%

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Shang

et al. 2020

Entropy

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“…To alleviate these phenomena, CEEMDAN [14] was introduced to decompose non-liner and non-stationary signals. Currently, this method is also widely applied in the fault diagnosis domain to obtain feature information on vibration signals [47,48]. The following is the basic principle of CEEMDAN:…”

Section: Ceemdan Algorithmmentioning

confidence: 99%

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Zhan

et al. 2019

Symmetry

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Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

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“…Torres M E and others put forward CEEMDAN method to solve this problem. This method adds adaptive white noise in every decomposing stage and calculates the only left residual signal to achieve every model component [14]. In this way, the problem of EEMD was solved.…”

Section: Ceemdan's Principlementioning

confidence: 99%

Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise

Xiao¹,

Cunyi²,

Wen³

et al. 2018

J. vibroeng.

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As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely.

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CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed

Cited by 37 publications

References 26 publications

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise

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