Stability Analysis of Tribosystem Based on the Energy Feature of Friction Vibration

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Ting Liu ◽  
Guobin Li ◽  
Haijun Wei ◽  
Pengfei Xing
Keyword(s):  
Stability Analysis ◽  
Phase Space ◽  
Singular Value Decomposition ◽  
Feature Vector ◽  
Singular Value ◽  
Vibration Energy ◽  
Feature Parameter ◽  
Energy Feature ◽  
Space Matrix ◽  
Value Decomposition

The tribosystem stability in different wear stages was analyzed by the friction vibration energy feature, which was extracted from the phase-space-matrix of friction vibration attractor with singular value decomposition (SVD). An energy feature parameter K of friction vibration was defined as the norm of the singular value feature vector, and the variation of K in different wear stages was also investigated in this paper. Results show that K becomes larger in the running-in wear stage, friction vibration energy becomes higher and tribosystem stability gets worse; K fluctuates smoothly and steadily in the stable wear stage, friction vibration energy is stable and the tribosystem is dynamically stable; and K increases sharply in the severe wear stage, the friction vibration energy increases dramatically and the tribosystem stability decreases greatly. Therefore, the friction vibration energy can reflect the tribosystem stability in different wear stages with the energy feature parameter K.

Roller Bearing Fault Diagnosis Based on ELMD and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1698-1700 ◽  
Author(s):  
Chang Liang Liu ◽  
Xiu Mei Huang ◽  
Xian Jin Luo
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Feature Vector ◽  
Vibration Signal ◽  
Singular Value ◽  
Fuzzy C Means ◽  
Bearing Fault Diagnosis ◽  
Fuzzy C Means Clustering ◽  
Value Decomposition ◽  
Vector Matrix

For the non-stationary characteristics of rotating machinery fault vibration signal, proposed a fault diagnosis method that based on ensemble local mean decomposition (ELMD) to extract fault feature, and fuzzy C-means clustering (FCM) to perform the fault identification. ELMD method can effectively solve the problem of aliasing modes in LMD. Firstly, decomposing the fault vibration signal by ELMD, PF components were obtained in which the initial feature vector matrix, The PF components compose a initial feature vector matrix, and do singular value decomposition, using the singular value decomposition feature vector as the fault characteristic vectors. Finally, using FCM clustering as a fault classifier. Achieved the identification of different fault types. Experimental results show that this method can effectively achieve the bearing fault diagnosis.

scholarly journals Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rui Liu
Keyword(s):  
Singular Value Decomposition ◽  
Unsupervised Learning ◽  
Feature Vector ◽  
Feature Fusion ◽  
Wavelet Packet ◽  
Basic Feature ◽  
Singular Value ◽  
Learning Method ◽  
Value Decomposition ◽  
Fusion Feature

The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.

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