Application of EEMD and improved frequency band entropy in bearing fault feature extraction

Li, Hua; Wu, Xing; Chen, Qing

doi:10.1016/j.isatra.2018.12.002

Cited by 90 publications

(38 citation statements)

References 31 publications

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“…Thus, it is defined as the eigenvalue of the reconstructed signals in this method. In [121], ensemble empirical mode decomposition is combined with improved frequency band entropy for bearing fault extraction. Zhang et al [122] propose a method based on empirical mode decomposition, clear iterative interval threshold, and the kernel-based fuzzy c-means eigenvalue extraction.…”

Section: Other Typical Entropy Theories Application On Bearingmentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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Section: Other Typical Entropy Theories Application On Bearingmentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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“…However, these approaches show poor distinguishability for insufficient fault features for nonlinear and non-stationary signals. Another powerful signal processing method for non-linear and non-stationary signals, named empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) [18], and complete ensemble empirical mode decomposition (CEEMD) [19], has been widely used to solve fault diagnosis of rotating machinery and circuit systems. Additionally, compared with wavelet transform where the basic functions are fixed, the EMD-based method decomposes signals according to time-scale characteristics of data without setting any basis function in advance, which has stronger local stationary.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Zhang

2020

Electronics

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Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy.

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“…However, EMD may produce modal aliasing and boundary effect in the decomposition process. EEMD was proposed by Wu and Huang [8], and applied in bearing fault feature extraction [9], which added white noise to the analyzed signal. Although EEMD improves the aliasing problem of EMD, it is still a recursive filtering algorithm like EMD.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on improved VMD and DCNN

Wang¹,

Xu²,

Liu³

2020

J. vibroeng.

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Vibration signal produced by rolling element bearings has obvious non-stationary and nonlinear characteristics, and it's necessary to preprocess the original signals to obtain better diagnostic results. This paper proposes an improved variational mode decomposition (IVMD) and deep convolutional neural network (DCNN) method to realize the intelligent fault diagnosis of rolling element bearings. Firstly, to solve the problem that the number of decomposed modes of variational mode decomposition (VMD) needs to be preset, an IVMD method is proposed, where the mode number can be determined adaptively according to the curve of the instantaneous frequency mean of mode functions. With this method, the vibration signal can be decomposed into a series of modal components containing bearing fault characteristic information. Then, DCNN is employed to fuse these multi-scale modal components, which can automatically learn fault features and establish bearing fault diagnosis model to realize intelligent fault diagnosis eventually. Experimental analysis and comparison results verify that the proposed method can effectively enhance the bearing fault features and improve the diagnosis accuracy.

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Application of EEMD and improved frequency band entropy in bearing fault feature extraction

Cited by 90 publications

References 31 publications

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Bearing fault diagnosis based on improved VMD and DCNN

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