The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

Liu, Fuzheng; Gao, Junwei; Liu, Huabo

doi:10.1109/access.2020.2968843

Cited by 52 publications

(28 citation statements)

References 26 publications

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“…Theoretical analysis and experiments show that CEEMD offers a precise reconstruction of time series and better spectral separation of IMFs than the other two methods of decomposition in the empirical mode. Every IMF component represents signal characteristics in various bands of high to low frequencies, and the IMF's contain all frequency components [23].…”

Section: Complementary Ensemble Empirical Mode Decomposition (Ceemd)mentioning

confidence: 99%

Transient-Based Leak Detection and Monitoring of Water Pipes Using Complementary Ensemble Empirical Mode Decomposition (CEEMD) Method

PiRemli

Ghazali

Azmi

et al. 2021

ARFMTS

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Transient signal occurs when there is sudden change of pressure inside the pipeline especially due to pressure surge or opening and closing valve. Decomposition method applied in this study to analyze the transient signal and remove noise that contaminate the signal. In this paper, an adaptive decomposition algorithm that is Complementary Ensemble Empirical Mode Decomposition (CEEMD) proposed in order to overcome the problem occur in Hilbert-Huang Transform (HHT) method. This method takes over the Empirical Mode Decomposition (EMD) method that purpose as pre-processing method in HHT. This improvement made to overcome mode mixing and reconstruction error that lies in EMD method. CEEMD method used to extract the IMF’s component of a contaminated signal to remove undesirable noise signal. Instantaneous analysis using Hilbert Transform (HT) apply for selected IMF to locate the feature’s position along the pipeline. The result prove that proposed decomposition method show success with the percentage of error between measured and analysed distance only below that 4% for all features extracted.

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Section: Complementary Ensemble Empirical Mode Decomposition (Ceemd)mentioning

confidence: 99%

Transient-Based Leak Detection and Monitoring of Water Pipes Using Complementary Ensemble Empirical Mode Decomposition (CEEMD) Method

PiRemli

Ghazali

Azmi

et al. 2021

ARFMTS

View full text Add to dashboard Cite

show abstract

“…Literature [5] uses the Short-time Fourier Transform (STFT) and generative neural networks methods to diagnose rolling bearing faults. Literature [9] uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Linearly Decreasing Particle Swarm Optimization Probabilistic Neural Network (LDWPSO-PNN) methods to analyze and compare the vibration signals of rotating machinery. Literature [10] uses the Variational Modal Decomposition Fractional Fourier Transform (VMD-FRFT) method to diagnose rolling bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Meng¹,

Wang²,

Yang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

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“…At present, the research methods of fault diagnosis are mainly divided into model-based methods, experiencebased methods, and intelligence-based methods [11][12][13][14][15][16]. Among them, the intelligence-based methods were also called the data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

You

Chen

Liu

et al. 2021

Shock and Vibration

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The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

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The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

Cited by 52 publications

References 26 publications

Transient-Based Leak Detection and Monitoring of Water Pipes Using Complementary Ensemble Empirical Mode Decomposition (CEEMD) Method

Transient-Based Leak Detection and Monitoring of Water Pipes Using Complementary Ensemble Empirical Mode Decomposition (CEEMD) Method

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

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