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Haiyang Pan
School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, 243032, PR China

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Journal article
Published: 19 July 2021 in Measurement Science and Technology
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ACS Style

Rui Wang; Hongliang Zhang; Ruilin Pan; Haiyang Pan. Singular value penalization based adversarial domain adaptation for fault diagnosis of rolling bearings. Measurement Science and Technology 2021, 1 .

AMA Style

Rui Wang, Hongliang Zhang, Ruilin Pan, Haiyang Pan. Singular value penalization based adversarial domain adaptation for fault diagnosis of rolling bearings. Measurement Science and Technology. 2021; ():1.

Chicago/Turabian Style

Rui Wang; Hongliang Zhang; Ruilin Pan; Haiyang Pan. 2021. "Singular value penalization based adversarial domain adaptation for fault diagnosis of rolling bearings." Measurement Science and Technology , no. : 1.

Journal article
Published: 18 March 2021 in IEEE Transactions on Instrumentation and Measurement
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ACS Style

Jinde Zheng; Xinglong Wang; Haiyang Pan; Jinyu Tong; Jun Zhang; Qingyun Liu. The Traverse Symplectic Correlation-Gram (TSCgram): A New and Effective Method of Optimal Demodulation Band Selection for Rolling Bearing. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -15.

AMA Style

Jinde Zheng, Xinglong Wang, Haiyang Pan, Jinyu Tong, Jun Zhang, Qingyun Liu. The Traverse Symplectic Correlation-Gram (TSCgram): A New and Effective Method of Optimal Demodulation Band Selection for Rolling Bearing. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-15.

Chicago/Turabian Style

Jinde Zheng; Xinglong Wang; Haiyang Pan; Jinyu Tong; Jun Zhang; Qingyun Liu. 2021. "The Traverse Symplectic Correlation-Gram (TSCgram): A New and Effective Method of Optimal Demodulation Band Selection for Rolling Bearing." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-15.

Journal article
Published: 11 March 2021 in Applied Soft Computing
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Support matrix machine (SMM) is a new and effective classification method, which has been applied in the field of image processing. In this paper, an improved SMM called symplectic hyperdisk matrix machine (SHMM) is proposed and applied to the roller bearing fault diagnosis. In SHMM, the symplectic geometry similarity transformation (SGST) is used to obtain the dimensionless feature matrix, which protects the signal structure information while weakening the interference of noise. Then, different types of hyperdisks are constructed to divide different types of data, several more realistic hyperdisk prediction models can be obtained and the problem of under estimation is avoided. In order to fully mine the spatial structure information, the feature matrix is mapped to the high-dimensional space by kernel technology, and the decision function is established by using the structure information hidden of the input matrix in the SHMM. Experimental results of three datasets of roller bearing show that, compared with symplectic geometry matrix machine (SGMM), SMM, support vector machine (SVM) and radial basis function (RBF) neural network methods, the proposed SHMM has good application effect in roller bearing fault diagnosis.

ACS Style

Haiyang Pan; Jinde Zheng. An intelligent fault diagnosis method for roller bearing using symplectic hyperdisk matrix machine. Applied Soft Computing 2021, 105, 107284 .

AMA Style

Haiyang Pan, Jinde Zheng. An intelligent fault diagnosis method for roller bearing using symplectic hyperdisk matrix machine. Applied Soft Computing. 2021; 105 ():107284.

Chicago/Turabian Style

Haiyang Pan; Jinde Zheng. 2021. "An intelligent fault diagnosis method for roller bearing using symplectic hyperdisk matrix machine." Applied Soft Computing 105, no. : 107284.

Research article
Published: 25 February 2021 in Journal of Vibration and Control
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Inspired by the empirical wavelet transform method, a newly nonstationary signal analysis method–termed order-statistic filtering Fourier decomposition is proposed in this article. First, order-statistic filtering Fourier decomposition uses order-statistic filtering and smoothing to preprocess the Fourier spectrum of original signal, which resolves the problem of unreasonable boundaries obtained by empirical wavelet transform in segmenting the Fourier spectrum. Then, the mono-components with physical significance are obtained by adaptively reconstructing the coefficient of fast Fourier transform in each interval, which improves the problem of too many false components obtained by Fourier decomposition method. The order-statistic filtering Fourier decomposition method is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition, empirical wavelet transform, Fourier decomposition method, and variational mode decomposition through analyzing simulation signals, and the result indicates that order-statistic filtering Fourier decomposition is much more accurate and reasonable in obtaining mono-components. After that, the order-statistic filtering Fourier decomposition method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness of order-statistic filtering Fourier decomposition to the comparative methods in bearing fault identification are verified.

ACS Style

Siqi Huang; Jinde Zheng; Haiyang Pan; Jinyu Tong. Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis. Journal of Vibration and Control 2021, 1 .

AMA Style

Siqi Huang, Jinde Zheng, Haiyang Pan, Jinyu Tong. Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis. Journal of Vibration and Control. 2021; ():1.

Chicago/Turabian Style

Siqi Huang; Jinde Zheng; Haiyang Pan; Jinyu Tong. 2021. "Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis." Journal of Vibration and Control , no. : 1.

Journal article
Published: 30 November 2020 in IEEE Access
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To solve the problem that the limited time-frequency features cannot fully represent the deep-seated state information of rolling bearing, the time-frequency analysis method, whale optimization algorithm (WOA) and support matrix machine (SMM) are combined, and a fault diagnosis model based on multisynchrosqueezing transform (MSST) and WOA-SMM is proposed. First, the time-frequency trait of the original signal is extracted by MSST. Then, using the time-frequency spectrum processed by MSST as the input of SMM, MSST solves the problem of state information loss when constructing a characteristic matrix. Finally, the parameters of the SMM are optimized by WOA and the ideal parameters can be obtained adaptively, and the problem of setting parameters subjectively is solved. The experimental analysis of the two datasets shows that WOA-SMM is superior not only to other classifiers in classification performance, but also has higher in convergence accuracy and speed for rolling bearing fault diagnosis.

ACS Style

Jinde Zheng; Mingen Gu; Haiyang Pan; Jinyu Tong. A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM. IEEE Access 2020, 8, 215355 -215364.

AMA Style

Jinde Zheng, Mingen Gu, Haiyang Pan, Jinyu Tong. A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM. IEEE Access. 2020; 8 (99):215355-215364.

Chicago/Turabian Style

Jinde Zheng; Mingen Gu; Haiyang Pan; Jinyu Tong. 2020. "A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM." IEEE Access 8, no. 99: 215355-215364.

Journal article
Published: 12 October 2020 in IEEE Access
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Machinery fault diagnosis tasks have been well addressed when sufficient and abundant data are available. However, the data imbalance problem widely exists in real-world scenarios, which leads to the performance deterioration of fault diagnosis markedly. To solve this problem, we present a novel imbalanced fault diagnosis method based on the enhanced generative adversarial networks (GAN). By artificially generating fake samples, the proposed method can mitigate the loss caused by the lack of real fault data. Specifically, in order to improve the quality of generated samples, a new discriminator is designed using spectrum normalization (SN) strategy and a two time-scale update rule (TTUR) method is used to stabilize the training process of GAN. Then, an enhanced Wasserstein GAN with gradient penalty is developed to generate high-quality synthetic samples for the fault samples set. Finally, a deep convolutional classifier is constructed to carry out fault classification. The performance and effectiveness of the proposed method are validated on the Case Western Reserve University bearing dataset and rolling bearing dataset acquired from our laboratory. The simulation results show that the proposed method has a superior performance than other methods for imbalanced fault diagnosis tasks.

ACS Style

Hongliang Zhang; Rui Wang; Ruilin Pan; Haiyang Pan. Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks. IEEE Access 2020, 8, 185950 -185963.

AMA Style

Hongliang Zhang, Rui Wang, Ruilin Pan, Haiyang Pan. Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks. IEEE Access. 2020; 8 (99):185950-185963.

Chicago/Turabian Style

Hongliang Zhang; Rui Wang; Ruilin Pan; Haiyang Pan. 2020. "Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks." IEEE Access 8, no. 99: 185950-185963.

Journal article
Published: 30 August 2020 in Mechanism and Machine Theory
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Traditional time-frequency analysis methods, including empirical mode decomposition (EMD), local characteristic-scale decomposition (LCD) and variable mode decomposition (VMD), have some limitations in nonlinear signal analysis. When the signal has strong noise, traditional time-frequency analysis methods will force the signal to be decomposed into several inaccurate components, and the achieved components usually suffer from the end effect problem. Considering the above pressing challenge, a new signal decomposition algorithm, nonlinear sparse mode decomposition (NSMD), is proposed in this protocol. The core of NSMD is that the local narrowband signal disappears under the action of the singular local linear operator, so the singular local linear operator can be applied to extract the local narrowband component of the detected signal. Meanwhile, the obtained local narrowband signal can be superposed as the basic signal to close to the original signal, realizing the adaptive decomposition of the signal with good robustness and adaptability. The analysis results of simulation signals and planetary gearbox fault signals indicate that the proposed NSMD method is effective for raw vibration signals.

ACS Style

Haiyang Pan; Jinde Zheng; Yu Yang; Junsheng Cheng. Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis. Mechanism and Machine Theory 2020, 155, 104082 .

AMA Style

Haiyang Pan, Jinde Zheng, Yu Yang, Junsheng Cheng. Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis. Mechanism and Machine Theory. 2020; 155 ():104082.

Chicago/Turabian Style

Haiyang Pan; Jinde Zheng; Yu Yang; Junsheng Cheng. 2020. "Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis." Mechanism and Machine Theory 155, no. : 104082.

Journal article
Published: 05 December 2019 in IEEE Access
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ACS Style

Haiyang Pan; Jinde Zheng. A Generalized Framework of Adaptive Mode Decomposition. IEEE Access 2019, 7, 176382 -176393.

AMA Style

Haiyang Pan, Jinde Zheng. A Generalized Framework of Adaptive Mode Decomposition. IEEE Access. 2019; 7 ():176382-176393.

Chicago/Turabian Style

Haiyang Pan; Jinde Zheng. 2019. "A Generalized Framework of Adaptive Mode Decomposition." IEEE Access 7, no. : 176382-176393.

Journal article
Published: 10 September 2019 in IEEE Access
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The vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectively decompose non-stationary and nonlinear signals, but it is not suitable for signal analysis of bearing with a complicated spectrum. In this paper, an improved EWT (IEWT) method is proposed by developing a new segmentation approach. Meanwhile, the IEWT is compared with empirical mode decomposition (EMD) and EWT to verify the superiority of IEWT in decomposition accuracy. By combining with the refined composite multiscale dispersion entropy (RCMDE), which is a powerful nonlinear tool for irregularity measurement of vibration signals, a new diagnosis method based on IEWT, RCMDE, multi-cluster feature selection and support vector machine is proposed. Then the method is applied to analysis of bearing in this paper and the results show that the new method has higher identifying rate and better performance than that of the methods of RCMDE combining with EMD or EWT. Also, the superiority of RCMDE to dispersion entropy and multiscale dispersion entropy is investigated, together with the superiority of MCFS for feature selection.

ACS Style

Jinde Zheng; Siqi Huang; Haiyang Pan; Kuosheng Jiang. An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing. IEEE Access 2019, 8, 168732 -168742.

AMA Style

Jinde Zheng, Siqi Huang, Haiyang Pan, Kuosheng Jiang. An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing. IEEE Access. 2019; 8 (99):168732-168742.

Chicago/Turabian Style

Jinde Zheng; Siqi Huang; Haiyang Pan; Kuosheng Jiang. 2019. "An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing." IEEE Access 8, no. 99: 168732-168742.

Journal article
Published: 04 July 2019 in IEEE Access
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Effective condition monitoring provides some benefits such as improving safety and reliability. Roller bearing is the key component of rotating machinery, and a novel roller bearing condition monitoring method based on rational Hermite interpolation-local characteristic-scale decomposition (RHLCD) and fusion variable predictive model based class discriminate method (FVPMCD) is proposed in this paper. RHLCD can adaptively decompose any a complex signal into a sum of rational intrinsic scale components (RISCs), whose instantaneous frequency has physical meaning. In addition, targeting the limitation of variable predictive model based class discriminate method (VPMCD), FVPMCD is presented. First, four kinds of common models are used to recognize a sample. Then, the recognition results of each model are satisfied, and the recognition probability of each state is calculated. Finally, the largest recognition probability of the state is chosen to recognize categories. The analysis results of experimental signals indicate that the proposed condition monitoring approach can identify the states of roller bearing effectively.

ACS Style

Haiyang Pan; Jinde Zheng; Qingyun Liu. A Novel Roller Bearing Condition Monitoring Method Based on RHLCD and FVPMCD. IEEE Access 2019, 7, 96753 -96763.

AMA Style

Haiyang Pan, Jinde Zheng, Qingyun Liu. A Novel Roller Bearing Condition Monitoring Method Based on RHLCD and FVPMCD. IEEE Access. 2019; 7 (99):96753-96763.

Chicago/Turabian Style

Haiyang Pan; Jinde Zheng; Qingyun Liu. 2019. "A Novel Roller Bearing Condition Monitoring Method Based on RHLCD and FVPMCD." IEEE Access 7, no. 99: 96753-96763.

Journal article
Published: 25 June 2019 in Entropy
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Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

ACS Style

Zhilin Dong; Jinde Zheng; Siqi Huang; Haiyang Pan; Qingyun Liu. Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing. Entropy 2019, 21, 621 .

AMA Style

Zhilin Dong, Jinde Zheng, Siqi Huang, Haiyang Pan, Qingyun Liu. Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing. Entropy. 2019; 21 (6):621.

Chicago/Turabian Style

Zhilin Dong; Jinde Zheng; Siqi Huang; Haiyang Pan; Qingyun Liu. 2019. "Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing." Entropy 21, no. 6: 621.

Journal article
Published: 27 May 2019 in Mechanism and Machine Theory
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In many classification problems such as roller bearing fault diagnosis, it is often met that input samples are two-dimensional matrices constructed by vibration signals, and the rows or columns in the input matrices are strongly correlated. Support matrix machine (SMM) is a new classifier with matrix as input, which has a good diagnostic effect by using of matrix structural information. Unfortunately, SMM algorithm is essentially binary, which need carry on the multiple binary classifications for multi-class classification problem. Meanwhile, SMM method has limitations in dealing with the complex input matrices, such as noise robustness and convergence problem. Therefore, a new classification method, called symplectic geometry matrix machine (SGMM), is proposed in this paper. In SGMM, by using symplectic geometry similarity transformation, the proposed method not only protects the original structure of the signal, but also automatically extracts noiseless features to establish weight coefficient model, which can achieve multi-class tasks. Meanwhile, because of establishment of weight coefficient model, the convergence problem can be avoided. The roller bearing fault signals are used to demonstrate the validity of the SGMM method, and the analysis results indicate that the proposed method has a good effectiveness in roller bearing fault diagnosis.

ACS Style

Haiyang Pan; Yu Yang; Jinde Zheng; Xin Li; Junsheng Cheng. A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine. Mechanism and Machine Theory 2019, 140, 31 -43.

AMA Style

Haiyang Pan, Yu Yang, Jinde Zheng, Xin Li, Junsheng Cheng. A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine. Mechanism and Machine Theory. 2019; 140 ():31-43.

Chicago/Turabian Style

Haiyang Pan; Yu Yang; Jinde Zheng; Xin Li; Junsheng Cheng. 2019. "A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine." Mechanism and Machine Theory 140, no. : 31-43.

Accepted manuscript
Published: 13 May 2019 in Measurement Science and Technology
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In the fault diagnosis of rolling bearing, the vibration signals, which are collected from the field test, are often more complex, because they unavoidably contain various noises and measurement errors, so "outliers" may occur in the features extracted from the collected vibration signals. Aiming at the above problems, the Agent Discriminate Model based Optimization Weighted (ADMOW) method is proposed. By using entropy weight method (EWM), the entropy weights of the sample features are calculated firstly, and the features are weighted to weaken the influence of "outliers" on the modeling. Secondly, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the model, and a more accurate classification model is obtained. Eventually, ADMOW is applied to recognize defaults of rolling bearings. The test results indicate that comparing several pattern recognition methods, the proposed method can effectively weaken the influence of "outliers" and improve the recognition rate and recognition accuracy.

ACS Style

Haiyang Pan; Jian Zhang; Jinde Zheng; Xiaolong Zhu; Ziwei Pan. Agent discriminate model based optimization weighted method and its application in fault diagnosis of rolling bearings. Measurement Science and Technology 2019, 30, 125904 .

AMA Style

Haiyang Pan, Jian Zhang, Jinde Zheng, Xiaolong Zhu, Ziwei Pan. Agent discriminate model based optimization weighted method and its application in fault diagnosis of rolling bearings. Measurement Science and Technology. 2019; 30 (12):125904.

Chicago/Turabian Style

Haiyang Pan; Jian Zhang; Jinde Zheng; Xiaolong Zhu; Ziwei Pan. 2019. "Agent discriminate model based optimization weighted method and its application in fault diagnosis of rolling bearings." Measurement Science and Technology 30, no. 12: 125904.

Journal article
Published: 28 March 2019 in IEEE Access
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ACS Style

Congzhi Li; Jinde Zheng; Haiyang Pan; Jinyu Tong; Yifang Zhang. Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing. IEEE Access 2019, 7, 47663 -47673.

AMA Style

Congzhi Li, Jinde Zheng, Haiyang Pan, Jinyu Tong, Yifang Zhang. Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing. IEEE Access. 2019; 7 ():47663-47673.

Chicago/Turabian Style

Congzhi Li; Jinde Zheng; Haiyang Pan; Jinyu Tong; Yifang Zhang. 2019. "Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing." IEEE Access 7, no. : 47663-47673.

Journal article
Published: 18 March 2019 in Entropy
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Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multiscale fuzzy entropy (CIMFE) is proposed by using cubic spline interpolation of the time series over different scales to overcome the drawbacks of the coarse-grained MFE process. The proposed CIMFE method is compared with MSE and MFE by analyzing simulation signals and the result indicates that CIMFE is more robust than MSE and MFE in analyzing short time series. Taking this into account, a new fault diagnosis method for rolling bearing is presented by combining CIMFE for feature extraction with Laplacian support vector machine for fault feature classification. Finally, the proposed fault diagnosis method is applied to the experiment data of rolling bearing by comparing with the MSE, MFE and other existing methods, and the recognition rate of the proposed method is 98.71%, 98.71%, 98.71%, 98.71% and 100% under different training samples (5, 10, 15, 20 and 25), which is higher than that of the existing methods.

ACS Style

Qingyun Liu; Haiyang Pan; Jinde Zheng; Jinyu Tong; Jiahan Bao. Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing. Entropy 2019, 21, 292 .

AMA Style

Qingyun Liu, Haiyang Pan, Jinde Zheng, Jinyu Tong, Jiahan Bao. Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing. Entropy. 2019; 21 (3):292.

Chicago/Turabian Style

Qingyun Liu; Haiyang Pan; Jinde Zheng; Jinyu Tong; Jiahan Bao. 2019. "Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing." Entropy 21, no. 3: 292.

Journal article
Published: 13 August 2018 in Entropy
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Multiscale entropy (MSE), as a complexity measurement method of time series, has been widely used to extract the fault information hidden in machinery vibration signals. However, the insufficient coarse graining in MSE will result in fault pattern information missing and the sample entropy used in MSE at larger factors will fluctuate heavily. Combining fractal theory and fuzzy entropy, the time shift multiscale fuzzy entropy (TSMFE) is put forward and applied to the complexity analysis of time series for enhancing the performance of MSE. Then TSMFE is used to extract the nonlinear fault features from vibration signals of rolling bearing. By combining TSMFE with the Laplacian support vector machine (LapSVM), which only needs very few marked samples for classification training, a new intelligent fault diagnosis method for rolling bearing is proposed. Also the proposed method is applied to the experiment data analysis of rolling bearing by comparing with the existing methods and the analysis results show that the proposed fault diagnosis method can effectively identify different states of rolling bearing and get the highest recognition rate among the existing methods.

ACS Style

Xiaolong Zhu; Jinde Zheng; Haiyang Pan; Jiahan Bao; Yifang Zhang. Time-Shift Multiscale Fuzzy Entropy and Laplacian Support Vector Machine Based Rolling Bearing Fault Diagnosis. Entropy 2018, 20, 602 .

AMA Style

Xiaolong Zhu, Jinde Zheng, Haiyang Pan, Jiahan Bao, Yifang Zhang. Time-Shift Multiscale Fuzzy Entropy and Laplacian Support Vector Machine Based Rolling Bearing Fault Diagnosis. Entropy. 2018; 20 (8):602.

Chicago/Turabian Style

Xiaolong Zhu; Jinde Zheng; Haiyang Pan; Jiahan Bao; Yifang Zhang. 2018. "Time-Shift Multiscale Fuzzy Entropy and Laplacian Support Vector Machine Based Rolling Bearing Fault Diagnosis." Entropy 20, no. 8: 602.

Journal article
Published: 11 May 2018 in Entropy
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As a nonlinear dynamic method for complexity measurement of time series, multiscale entropy (MSE) has been successfully applied to fault diagnosis of rolling bearings. However, the MSE algorithm is sensitive to the predetermined parameters and depends heavily on the length of the time series and MSE may yield an inaccurate estimation of entropy or undefined entropy when the length of time series is too short. To improve the robustness of complexity measurement for short time series, a novel nonlinear parameter named multiscale distribution entropy (MDE) was proposed and employed to extract the nonlinear complexity features from vibration signals of rolling bearing in this paper. Combining with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension reduction and Kriging-variable predictive models based class discrimination (KVPMCD) for automatic identification, a new intelligent fault diagnosis method for rolling bearings was proposed. Finally, the proposed approach was applied to analyze the experimental data of rolling bearings and the results indicated that the proposed method could distinguish the different fault categories of rolling bearings effectively.

ACS Style

Deyu Tu; Jinde Zheng; Zhanwei Jiang; Haiyang Pan. Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings. Entropy 2018, 20, 360 .

AMA Style

Deyu Tu, Jinde Zheng, Zhanwei Jiang, Haiyang Pan. Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings. Entropy. 2018; 20 (5):360.

Chicago/Turabian Style

Deyu Tu; Jinde Zheng; Zhanwei Jiang; Haiyang Pan. 2018. "Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings." Entropy 20, no. 5: 360.

Journal article
Published: 02 November 2017 in Entropy
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The vibration signals of rolling bearings are often nonlinear and non-stationary. Multiscale entropy (MSE) has been widely applied to measure the complexity of nonlinear mechanical vibration signals, however, at present only the single channel vibration signals are used for fault diagnosis by many scholars. In this paper multiscale entropy in multivariate framework, i.e., multivariate multiscale entropy (MMSE) is introduced to machinery fault diagnosis to improve the efficiency of fault identification as much as possible through using multi-channel vibration information. MMSE evaluates the multivariate complexity of synchronous multi-channel data and is an effective method for measuring complexity and mutual nonlinear dynamic relationship, but its statistical stability is poor. Refined composite multivariate multiscale fuzzy entropy (RCMMFE) was developed to overcome the problems existing in MMSE and was compared with MSE, multiscale fuzzy entropy, MMSE and multivariate multiscale fuzzy entropy by analyzing simulation data. Finally, a new fault diagnosis method for rolling bearing was proposed based on RCMMFE for fault feature extraction, Laplacian score and particle swarm optimization support vector machine (PSO-SVM) for automatic fault mode identification. The proposed method was compared with the existing methods by analyzing experimental data analysis and the results indicate its effectiveness and superiority.

ACS Style

Jinde Zheng; Deyu Tu; Haiyang Pan; Xiaolei Hu; Tao Liu; Qingyun Liu. A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings. Entropy 2017, 19, 585 .

AMA Style

Jinde Zheng, Deyu Tu, Haiyang Pan, Xiaolei Hu, Tao Liu, Qingyun Liu. A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings. Entropy. 2017; 19 (11):585.

Chicago/Turabian Style

Jinde Zheng; Deyu Tu; Haiyang Pan; Xiaolei Hu; Tao Liu; Qingyun Liu. 2017. "A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings." Entropy 19, no. 11: 585.