Application of Least Squares Support Vector Machine in Fault Diagnosis

2011 ◽  
pp. 192-200 ◽  
Author(s):  
Yongli Zhang ◽  
Yanwei Zhu ◽  
Shufei Lin ◽  
Xiaohong Liu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Support Vector

scholarly journals A new hybrid model of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine for fault diagnosis of gear pump

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402092204
Author(s):  
Yan Lu ◽  
Zhiping Huang
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Support Vector ◽  
Gear Pump ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Adaptive Dynamic ◽  
Empirical Wavelet Transform

Gear pump is the key component in hydraulic drive system, and it is very significant to fault diagnosis for gear pump. The combination of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine is proposed for fault diagnosis of gear pump in this article. Sparsity empirical wavelet transform is used to obtain the features of the vibrational signal of gear pump, the sparsity function is potential to make empirical wavelet transform adaptive, and adaptive dynamic least squares support vector machine is used to recognize the state of gear pump. The experimental results show that the diagnosis accuracies of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine are better than those of the empirical wavelet transform and adaptive dynamic least squares support vector machine method or the empirical wavelet transform and least squares support vector machine method.

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

Fault Diagnosis Method of Gear Based on Local Mean Decomposition and Least Squares Support Vector Machine

2013 ◽  
Vol 415 ◽  
pp. 548-554
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Zhi Rong Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Vibration Signal ◽  
Support Vector ◽  
Vibrational Signal ◽  
Local Mean Decomposition ◽  
Gear Fault ◽  
Input Sample ◽  
Local Mean

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

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