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A Strategy Using Variational Mode Decomposition, L-Kurtosis and Minimum Entropy Deconvolution to Detect Mechanical Faults

Hui Liu, Jiawei Xiang
2019 IEEE Access  
In this paper, a novel strategy using variational mode decomposition (VMD), L-Kurtosis and minimum entropy deconvolution (MED) is proposed to detect mechanical faults.  ...  INDEX TERMS Variational mode decomposition, L-Kurtosis, minimum entropy deconvolution, rotary mechanical component, fault detection. 70564 2169-3536  ...  THE STRARTEGY USING VMD, L-KURTOSIS AND MED In order to identify the faults of mechanical components (bearings and gears), a novel fault detection strategy using VMD, L-Kurtosis and MED is proposed.  ... 
doi:10.1109/access.2019.2920064 fatcat:lkvt5zqtvzet3mav5zkvbrrjbq

Feature extraction based on EWT with scale space threshold and improved MCKD for fault diagnosis

Lingfeng Li, Aibin Guo, Huayue Chen
2021 IEEE Access  
STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper.  ...  Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T .  ...  ACKNOWLEDGMENT The authors would like to thank all the reviewers for their constructive comments.  ... 
doi:10.1109/access.2021.3065307 fatcat:urrkshkz6bajtk4qytqflzhspq

A Novel Fault Diagnosis Method for Rotating Machinery Based on EEMD and MCKD

Z.-L. Lv, B.-P. Tang, Y. Zhou
2015 International Journal of Simulation Modelling  
In the present work, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and maximum correlated kurtosis deconvolution (MCKD) is proposed in order to solve this problem  ...  The experimental results indicate that, using the proposed method, the fault impulsive components in the obtained intrinsic mode functions (IMFs) with EEMD can be adaptively enhanced, and the weak fault  ...  By using the correlated kurtosis deconvolution of high-order shift, the capability of fault detection can be improved.  ... 
doi:10.2507/ijsimm14(3)6.298 fatcat:eodfz5s6q5athmoju2l67ae5zm

An Adaptive VMD Method Based on Improved GOA to Extract Early Fault Feature of Rolling Bearings

Chengjiang Zhou, Jun Ma, Jiande Wu, Xuyi Yuan
2019 International Journal of Innovative Computing, Information and Control  
In order to identify the early fault of bearing, an early feature extraction method based on adaptive variational mode decomposition (VMD) is proposed.  ...  Then, energy entropy mutual information (EEMI) index is introduced to consider the energy distribution of modes and the dependence between modes and the original signal.  ...  The authors sincerely express thanks to the reviewers for taking the time to review the paper in a busy schedule.  ... 
doi:10.24507/ijicic.15.04.1485 fatcat:x3nean64xjerzkugnoxih6q3ae

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei, Li, Xu, Huang
2019 Entropy  
The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis.  ...  A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings.  ...  Acknowledgments: Authors are grateful to all the reviewers and the editor for their valuable comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e21040409 pmid:33267123 pmcid:PMC7514898 fatcat:drfigyw7gzemfirsswsjuzfrii

Application of a novel improved adaptive CYCBD method in gearbox compound fault diagnosis

Huer Sun, Fuwang Liang, Yutao Liu, Kexin Liu, Zhijian Wang, Tianyuan Zhang, Jiyang Zhu, Yang Zhao
2021 IEEE Access  
Blind deconvolution theory is an effective fault diagnosis technique that includes minimum entropy deconvolution (MED) [1] , maximum correlated kurtosis deconvolution (MCKD) [2] , Optimal minimum entropy  ...  Compared with wavelet transform theory [20] , empirical mode decomposition (EMD) [21] , local average decomposition (LMD) [22] , variational mode decomposition (VMD) [23, 24] , singular spectrum decomposition  ... 
doi:10.1109/access.2021.3113515 fatcat:xf2zcf7lvna4vfwova3tars7he

Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition

Ying Zhang, Anchen Wang
2020 Shock and Vibration  
This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF).  ...  First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization  ...  Acknowledgments is research was supported by the Joint Funds of the National Natural Science Foundation of China (U1733201 and U1933202) and the Fundamental Research Funds for the Central Universities  ... 
doi:10.1155/2020/6216903 fatcat:owiulwgjavhwhmitvvoipgycbe

Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis

Yi Zhang, Yong Lv, Mao Ge
2021 Energy Reports  
Fault diagnosis a b s t r a c t A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault  ...  To address this circumstance, this paper introduces the particle swarm algorithm (PSO) to solve the optimal deconvolution parameters and proposes EMCKD.  ...  The authors also appreciate the free download of the original bearing failure data and two photos  ... 
doi:10.1016/j.egyr.2021.04.045 fatcat:qgyc4btlznde5fbxp7rxlw3tue

Rolling bearing Sub-health Recognition Using Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Hao Luo, Chao He, Jianing Zhou, Li Zhang
2021 IEEE Access  
Secondly, Maximum Correlation Kurtosis Deconvolution (MCKD) optimized by the improved parameters is used to process the incipient vibration signals with nonlinearity, nonstationary, and IFWA is used to  ...  adaptively adjust to the period T and the filter length L in MCKD(IFWA-MCKD).  ...  Enhanced Singular Spectrum Decomposition [2] (ESSD), Spectral Theory of Multidimensional Matrix [3] (STMM), Variational Mode Decomposition [4] (VMD) and others are used to increase the pulse numbers  ... 
doi:10.1109/access.2021.3064962 fatcat:sdnyk7vn5fgxlaiulb2bpwg37y

Novel Adaptive Sparse-spike Deconvolution Bearing Fault Detection Method Based on Curvelet Transform

Yanfeng Li, Zhijian Wang, Tiansheng Zhao, Yuan Zhao
2020 IEEE Access  
This method proposed in this paper is applied to the simulation signal and the vibration signal of rolling bearing fault, and is compared with the ASSD and the minimum entropy deconvolution (MED) to verify  ...  This paper has proposed a novel bearing fault detection method about adaptive Sparse-spike Deconvolution based on Curvelet Transform (CTSSD), where the novel technique about adaptive Sparse-spike Deconvolution  ...  and minimum entropy deconvolution (MED) to detect mechanical faults.  ... 
doi:10.1109/access.2020.3048127 fatcat:o4hukp5fsnhbbfjopr5qmzgiwq

Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests

Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, Yang Xukang, Moon G. Lee
2021 Shock and Vibration  
Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed.  ...  A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing  ...  Liu et al. proposed a loose-slip fault diagnosis method for axial piston pumps based on minimum entropy deconvolution, ensemble empirical mode decomposition, and extreme learning machine [20] .  ... 
doi:10.1155/2021/6649603 fatcat:chpwshlg2vaivm7iwnqlsab5m4

Author Index

2021 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)  
Bi, Xinjie Visibility Graph based Feature Extraction for Fault Diagnosis of Rolling Bearings [570256] Bin, Jie Optimization Method of Virtual Sand Table Background Server Based on Unity 3D [571175] C  ...  Spectrum Method Combined With Singular Value Decomposition for Weak Signal Detection [570389] A Novel Strategy for Signal Denoising Using Two-layer SVD and Its Application to Rub-impact Fault Diagnosis  ...  Kurtosis and Multi-point Optimal Minimum Entropy Deconvolution Adjusted [570245]Optimization of Axial Contact Stiffness of Precision Ball Screws Based on Hertzian Theory [570268]Research on Extraction  ... 
doi:10.1109/phm-nanjing52125.2021.9612757 fatcat:h4xp5wbdvjdpvmsri2dktwupbi

Global and Local Feature Extraction Using a Convolutional Autoencoder and Neural Networks for Diagnosing Centrifugal Pump Mechanical Faults

Alexander E. Prosvirin, Zahoor Ahmad, Jong-Myon Kim
2021 IEEE Access  
Centrifugal pumps are important types of electro-mechanical machines used for fluid and energy conveyance.  ...  Mechanical faults in centrifugal pumps lead to abnormal impacts in the vibration signal of the system.  ...  Content may change prior to final publication.  ... 
doi:10.1109/access.2021.3076571 fatcat:aqciwrrdmvc5pdexxk5jghxfwa

Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

Decai Zhang, Xueping Ren, Hanyue Zuo, Giuseppe Ruta
2021 Shock and Vibration  
Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox.  ...  PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected.  ...  [13] proposed a gear fault diagnosis method based on kurtosis criterion, variant modal decomposition (VMD), and self-organizing map (SOM) neural network. is method uses the VMD algorithm to decompose  ... 
doi:10.1155/2021/6669006 fatcat:a252fjx665asrgerghrmrzb2oq

Blind source separation of rolling element bearing' single channel compound fault based on Shift Invariant Sparse Coding

Hongchao Wang, Liwei Li, Xiaoyun Gong, Wenliao Du
2017 Journal of Vibroengineering  
The mechanical vibration source signal collected by sensor often includes a variety of internal vibration source of contributions such as gears, bearings, shaft and so on.  ...  It is often hoped to achieve effective separation of the source signal in order to obtain better fault diagnosis result.  ...  A novel method based on the optimal variational mode decomposition and 1.5-dimension envelope spectrum was proposed for detecting the compound fault of rotating machinery [14] .  ... 
doi:10.21595/jve.2016.17817 fatcat:oeojblsxm5g7dczbxabv5mrmv4
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