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Fault Diagnosis of Rolling Bearings Based on Improved Empirical Mode Decomposition and Fuzzy C-Means Algorithm

Hailun Wang, Fei Wu, Lu Zhang
2021 Traitement du signal  
To solve the problem, this paper proposes a way to recognize rolling bearing faults based on improved variational modal decomposition (VMD) and fuzzy c-means (FCM) algorithm.  ...  Several experiments were carried out to compare the improved VMD with empirical mode decomposition (EMD) and local mean decomposition (LMD) in the fault recognition and classification of rolling bearings  ...  In recent years, empirical mode decomposition (EMD) and local mean decomposition (LMD) have been widely applied to extract the features for fault diagnosis [3] .  ... 
doi:10.18280/ts.380217 fatcat:dh4o7vjl7jh4vfzgkn4n5xfiee

Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals

Yong Chang, Guangqing Bao, Sikai Cheng, Ting He, Qiaoling Yang
2021 IET Signal Processing  
C-means (PSO-KFCM) and variational mode decomposition (VMD).  ...  In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy  ...  A novel fault diagnosis approach of rolling bearing is put forward herein that combines particle swarm optimisation kernel fuzzy C-means (PSO-KFCM) and variational mode decomposition (VMD).  ... 
doi:10.1049/sil2.12026 fatcat:dpah4n2oj5aa5eyycqio3a2yxq

A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

Jingbao Hou, Yunxin Wu, Hai Gong, A. S. Ahmad, Lei Liu
2020 Applied Sciences  
A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering  ...  For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category.  ...  A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath-Geva (GG) clustering  ... 
doi:10.3390/app10010386 fatcat:c3tm5gcgzvh7jn6fds6s3xc66u

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Huimin Zhao, Meng Sun, Wu Deng, Xinhua Yang
2016 Entropy  
Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to  ...  and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e19010014 fatcat:f3fntwv2pbajpo2agg5dnlpbqi

A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

Ning Liu, Bing Liu, Jiaxin Wei, Cungen Xi
2020 Journal of Vibroengineering  
Therefore, a fault pattern recognition method for rolling bearing based on complete ensemble local mean decomposition with adaptive noise (CELMDAN) and fuzzy entropy is deeply studied.  ...  Then, the principle and properties of the fuzzy entropy are introduced in detail, and the fault feature of rolling bearing can be reflected.  ...  Acknowledgements This work was partly supported by the Special project of science and technology innovation of Tiandi Science and Technology (Grant No. 2018-TD-MS033).  ... 
doi:10.21595/jve.2020.21282 fatcat:oxb4mnyi4zc7jihk4bp2r73zfq

A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering

Jingbao Hou, Yunxin Wu, A. S. Ahmad, Hai Gong, Lei Liu
2021 IEEE Access  
DISCUSSION In order to efficiently perform fault diagnosis on rolling bearings, a fault diagnosis method based on clustering and parameter adaptive selection is proposed in this paper.  ...  In addition, the fault diagnosis method in this paper is based on rolling bearings, and there may be some disadvantages for the fault diagnosis of other mechanical mechanisms, and it is often necessary  ...  His interests include mechanical fault diagnosis, mechanical dynamics theory and application, mechanical behavior of metallurgical machinery, residual stress detection and reduction of aluminum alloy materials  ... 
doi:10.1109/access.2021.3096723 fatcat:z6xqpvxm5ra5jfp62qm7xbz3wu

Feature Cognitive Model Combined by An Improved Variational Mode and Singular Value Decomposition for Fault Signals

Jinxiang Chen, Zhu Zhu, Xiaoda Zhang
2020 Cognitive Computation and Systems  
The supervised learning-support vector machine and the unsupervised learning-fuzzy c-means clustering are used to verify the effectiveness of the presented method.  ...  A feature cognitive model combined with an improved variational mode and singular value decomposition is presented to recognise the characteristics of the fault signals from vibration signals of mechanical  ...  Constructing feature cognitive model combined by an improved variational mode and singular value decomposition Fault diagnosis based on feature extraction methods of the variational mode and SVDs can be  ... 
doi:10.1049/ccs.2020.0009 fatcat:4duyjqxmavdfvea7ijaegoxdji

Weak Fault Feature Extraction of Rolling Bearings Based on Adaptive Variational Modal Decomposition and Multiscale Fuzzy Entropy

Zhongliang Lv, Senping Han, Linhao Peng, Lin Yang, Yujiang Cao
2022 Sensors  
on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this  ...  The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22124504 fatcat:63bwpruulbds5hhthzrnatsd7q

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu, Zhi, Zhang, Guo, Peng, Liu
2019 Entropy  
For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods.  ...  Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e21111061 fatcat:zz7ii3sd6faxfnc5lll34rs2j4

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

Lingfeng Li, Aibin Guo, Huayue Chen
2021 IEEE Access  
Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (  ...  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.  ...  The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2018b produced by the Math-Works, Inc.  ... 
doi:10.1109/access.2021.3065307 fatcat:urrkshkz6bajtk4qytqflzhspq

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

Wei, Li, Xu, Huang
2019 Entropy  
After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based  ...  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

An improved bearing fault diagnosis scheme based on Hierarchical fuzzy entropy and Alexnet network

Xiaoyu Shi, Gen Qiu, Chun Yin, Xuegang Huang, Kai Chen, Yuhua Cheng, Shouming Zhong
2021 IEEE Access  
CONCLUSION This paper presented a fault diagnosis method based on variational mode decomposition and hierarchical fuzzy entropy for the bearing signals.  ...  Finally, based on Alexnet neural network for fault diagnosis and classification, compared with CNN and Googlenet neural network algorithms, the accuracy of fault diagnosis is greatly improved.  ... 
doi:10.1109/access.2021.3073708 fatcat:rzns2gw6ifc4fgkvb6e2qgz6pq

An Improved Kernel K-Mean Cluster Method and Its Application in Fault Diagnosis of Roller Bearing

Ling-Li Jiang, Yu-Xiang Cao, Hua-Kui Yin, Kong-Shu Deng
2013 Engineering  
The improved method is used for fault diagnosis of roller bearing that achieves a good cluster and diagnosis result, which demonstrates the effectiveness of the proposed method.  ...  For the kernel K-mean cluster method is run in an implicit feature space, the initial and iterative cluster centers cannot be defined explicitly.  ...  Feature database 3: The first eight intrinsic mode functions (IMFs) energy features of rolling bearing based on empirical mode decomposition (EMD).  ... 
doi:10.4236/eng.2013.51007 fatcat:ffmwfo7vmndg3hbhv2gjfm6rqy

Research on a new hybrid intelligent fault diagnosis method and its application

Zhenhua Wang, Zhentao Liu, Xueyan Lan, Jian Liu, Shaowei Wang, Yangming Wu, Yanbing Xue
2016 International Journal of Smart Home  
The experiment results show that the IMPSO algorithm can effectively optimize the weights of RBFNN, the IMPSO-RBFNN method can accurately realize high precision fault diagnosis of rolling bearing.  ...  The experiment results show that the IMPSO algorithm can effectively optimize the weights of RBFNN, the IMPSO-RBFNN method can obtain the higher fault diagnosis correctness rate for rolling bearing than  ...  Acknowledgments This research was supported by the Science and Technology Program of Dazhou (Research on the key technology of intelligent fault diagnosis for spindle system of large and middle NCM), China  ... 
doi:10.14257/ijsh.2016.10.7.14 fatcat:c2fum6gidjazljviuv3hbo6jze

Bearing fault diagnosis based on improved VMD and DCNN

Ran Wang, Lei Xu, Fengkai Liu
2020 Journal of Vibroengineering  
This paper proposes an improved variational mode decomposition (IVMD) and deep convolutional neural network (DCNN) method to realize the intelligent fault diagnosis of rolling element bearings.  ...  Experimental analysis and comparison results verify that the proposed method can effectively enhance the bearing fault features and improve the diagnosis accuracy.  ...  Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 51505277).  ... 
doi:10.21595/jve.2020.21187 fatcat:mi3m4nk53nbi7g6hnithewiofe
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