A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Zhang, Weibo; Zhou, Jing

doi:10.3390/e21070680

Cited by 38 publications

(19 citation statements)

References 43 publications

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“…Hence, according to the obtained optimal parameters C and g by the proposed LCPGWO method, the SVM model was trained and employed to achieve fault identification. For a dependable verification of the proposed method about the effectiveness and superiority, each of these comparative fault identifi- After the IMFs of all samples were obtained through signal decomposition, fault feature vectors were constructed by calculating ARCMDE values, where parameters should be chosen properly beforehand [51,52]. Here, four parameters were set in advance, which were embedding dimension m, number of class c, maximum scale factor τ max , and time delay t d .…”

Section: Application To Fault Identification Of Rolling Bearingsmentioning

confidence: 99%

“…After the IMFs of all samples were obtained through signal decomposition, fault feature vectors were constructed by calculating ARCMDE values, where parameters should be chosen properly beforehand [ 51 , 52 ]. Here, four parameters were set in advance, which were embedding dimension m , number of class c , maximum scale factor

, and time delay

.…”

Section: Engineering Applicationmentioning

confidence: 99%

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Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Shi

et al. 2021

Entropy

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Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

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Section: Application To Fault Identification Of Rolling Bearingsmentioning

confidence: 99%

, and time delay

.…”

Section: Engineering Applicationmentioning

confidence: 99%

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Shi

et al. 2021

Entropy

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show abstract

“…Essentially, the improved multi-scale dispersion entropy is composite multi-scale dispersion entropy (CMDE). In [22][23], refined composite multi-scale dispersion entropy (RCMDE) was respectively used to analyze the bearing vibration signals and biomedical signals.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Ensemble Composite Multi-Scale Dispersion Entropy and Density Peaks Clustering

Qin

Mao

et al. 2021

IEEE Access

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For bearing fault diagnosis, how to effectively extract informative fault information and accurately diagnose faults is still a key problem. To this end, this study proposes a novel bearing fault diagnosis approach based on ensemble composite multi-scale dispersion entropy (ECMDE), local preserving projections and density peaks clustering. Specifically, ECMDEs are developed to capture multi-scale fault features from the raw vibration signals. The goal of ECMDEs is to synthesize different kinds of composite multi-scale dispersion entropies to find more effective fault information. Subsequently, the local preserving projections method is utilized to reduce high-dimensional feature set and extract the effective fault information. Finally, the reduced features are fed into the density peaks clustering method to obtain the fault diagnosis results. Two experimental cases and extensive comparisons are applied to validate the effectiveness and noise robustness of the proposed method. Experimental results demonstrate that the proposed method is capable to reliably extract effective fault information of raw vibration signals and accurately diagnose bearing faults even under low signal-to-noise ratio conditions. INDEX TERMSEnsemble composite multi-scale dispersion entropy, local preserving projections, density peaks clustering, noise robustness, fault diagnosis ACRONYMS AND ABBREVIATIONS APC Affinity Propagation Clustering CMDE Composite Multi-scale Dispersion Entropy CMPE Composite Multi-scale Permutation Entropy CMSE Composite Multi-scale Sample Entropy

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“…us, the entropy-based method is usually connected with the time-frequency processing method to reach a more comprehensive and detailed analysis for sake of highlighting the inherent characteristics of the vibration signal while extracting multiscale features [18]. Fourier transform (FT) is a commonly used signal analysis method while it cannot analyze the signal's time domain and frequency domain part simultaneously due to the uncertainty principle.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

Liu

Yang

Shen

2020

Shock and Vibration

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The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

show abstract

A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Cited by 38 publications

References 43 publications

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Bearing Fault Diagnosis Method Based on Ensemble Composite Multi-Scale Dispersion Entropy and Density Peaks Clustering

An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

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