scholarly journals Running Status Diagnosis of S700K Turnout Based on VMD-KPCA and Fuzzy Clustering

2021 ◽  
Vol 23 (4) ◽  
pp. 327-335
Author(s):  
Zheng Li ◽  
Wenjun Wei ◽  
Xiaochun Wu ◽  
Yang Liu ◽  
Jinbo Yu
Keyword(s):  
Fuzzy Clustering ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Permutation Entropy ◽  
Power Curve ◽  
Limited Bandwidth ◽  
Railway Line ◽  
Multi Scale ◽  
Fuzzy Clustering Analysis ◽  
Mode Decomposition

S700K turnout is the key equipment of railway line conversion. The diagnosis of S700K turnout in a normal, sub-health, and fault running state is the primary premise to ensure the safe operation of the railway. Aiming at the consistency between the characteristics of the power curve of S700K turnout and its state information, this paper proposes a new algorithm based on variational mode decomposition (VMD) and kernel principal component analysis (KPCA) to extract the characteristics of the power curve of S700K turnout. It uses fuzzy clustering analysis to diagnose the running state of S700K turnout. First, to extract the detailed components of the action power curve, it is decomposed into intrinsic mode function with limited bandwidth (BIMF) by VMD. Secondly, the multi-scale permutation entropy (MPE) is used to characterize the signal complexity of the power curve and different BIMF components, which are taken as the running state feature set. After KPCA analysis, eigenvalues with a contribution rate greater than 95% are selected as the state eigenvector. The experimental results show that the diagnosis algorithm can effectively identify the running state of S700K turnout, meet the characteristics of fewer fault samples of S700K turnout, and do not need to train in advance, which is of great significance for field guidance.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

scholarly journals Recognition of denatured biological tissue based on variational mode decomposition and multi-scale permutation entropy

2019 ◽  
Vol 68 (2) ◽  
pp. 028702
Author(s):  
Bei Liu ◽  
Wei-Peng Hu ◽  
Xiao Zou ◽  
Ya-Jun Ding ◽  
Sheng-You Qian
Keyword(s):  
Biological Tissue ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Multi Scale ◽  
Mode Decomposition
Sign in / Sign up

Export Citation Format

Share Document