A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing

Deng, Wu; Zhang, Shengjie; Zhao, Huimin; Yang, Xinhua

doi:10.1109/access.2018.2834540

Cited by 254 publications

(145 citation statements)

References 50 publications

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“…Assume that the sample set in the leaf node j is defined as ={ | ( ) = }, where q( ) represents the tree structure from the root to the leaf node j in the decision tree, (19) can be transformed into the following formula, as show iñ…”

Section: Xgboostmentioning

confidence: 99%

“…And then the results of all subtasks are accumulated as the final result. Based on this idea, a "decomposition and ensemble" framework was proposed and widely applied to the analysis of time series, such as energy forecasting [16,17], fault diagnosis [18][19][20], and biosignal analysis [21][22][23]. This framework consists of three stages.…”

Section: Introductionmentioning

confidence: 99%

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A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

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Section: Xgboostmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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“…Although sewage treatment technology is more and more advanced and the quality standard of recycled water is higher, there are still many urban residents who refuse to use recycled water [5]. Many scholars have studied this issue from different perspectives [6][7][8]. Some scholars have found that technology is not the biggest obstacle for the promotion and popularization of recycled water, but the resistance and rejection of urban residents towards the use of recycled water [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Effect of Cognitive Variables and Emotional Variables on Urban Residents’ Recycled Water Reuse Behavior

Gao

Khan

2019

Sustainability

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Urban residents’ perception of recycled water reuse is the foundation for the realization of recycled water reuse behavior. However, even though the perception factor is equipped, it does not mean that urban residents will use recycled water continuously for sure. Therefore, in this research, the authors try to put cognitive factors and emotional factors into a unified behavioral process. Based on this theory framework, the paper will interpret the initiation, formation and continuing process of recycled water reuse behavior of urban residents. On the basis of previous studies, this study established a theoretical model to study the influence of cognitive factors and emotional factors on recycled water reuse behavior of the residents. Based on the data of 325 samples, the direct and indirect relationship between the variables in the model is verified through path analysis and mediation analysis. The empirical results show that: firstly, the urban residents’ perception of recycled water reuse can activate their emotion for recycled water, and the emotion includes both positive emotion and negative emotion; secondly, although the recognition of recycled water can stimulate both positive and negative emotional factors, there are great differences between positive and negative emotions on the initiation, formation and sustainability of recycled water behavior. Negative emotion has a certain effect on the initiation of recycled water reuse behavior, but it has no significant effect on the formation and sustainability of recycled water reuse behavior. By contrast, positive emotion has no significant effect on the initiation of recycled water reuse behavior, but it has a significant effect on the formation and sustainability of recycled water reuse behavior. That is to say, at different stages, the recycled water reuse behaviors are affected differently by positive emotions and negative emotions. Thirdly, compared with negative emotional variables, positive emotions have a greater impact on individual recycled water reuse behavior. Positive emotional variables can significantly mediate the impact of cognitive variables on recycled water reuse behavior habits. In other words, positive emotions play a vital role in the sustainability of recycled water reuse.

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“…However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing

Cited by 254 publications

References 50 publications

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Effect of Cognitive Variables and Emotional Variables on Urban Residents’ Recycled Water Reuse Behavior

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

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