Fault Diagnosis for Rotator in Rotating Machinery Based on Support Vector Machine

2014 ◽  
Vol 532 ◽  
pp. 102-105
Author(s):  
Wei Niu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Rotating Machinery ◽  
Support Vector ◽  
Swarm Optimization ◽  
Rotor Winding

As failure of rotator in rotating machinery has a certain concealment, fault diagnosis for rotator in rotating machinery based on support vector machine with particle swarm optimization algorithm is presented in the paper. And particle swarm optimization algorithm is applied to select the suitable parameters of support vector machine. In the study, we employ three PSO-SVM classifiers to recognize the four states of rotator in rotating machinery including normal state, rotor imbalance, rotor winding and rotor misalignment. More than 70 cases are used to testify the effectiveness of the PSO and SVM model compared with other classification models. The experimental results show that diagnostic precision for rotating machinery of PSO and SVM than that of SVM and BPNN.

Rapid Screening of Thyroid Dysfunction Using Raman Spectroscopy Combined with an Improved Support Vector Machine

2020 ◽  
Vol 74 (6) ◽  
pp. 674-683 ◽  
Author(s):  
Dingding Wang ◽  
Jing Jiang ◽  
Jiaqing Mo ◽  
Jun Tang ◽  
Xiaoyi Lv
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Thyroid Dysfunction ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Serum Samples ◽  
Swarm Optimization

This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic particle swarm optimization algorithm based on partial least squares is proposed to optimize support vector machine (PLS-GAPSO-SVM). In order to evaluate the performance of the algorithm, five optimization algorithms are used: grid search-based SVM (Grid-SVM), particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), artificial fish coupled uniform design algorithm-based SVM (AFUD-SVM), and simulated annealing particle swarm optimization algorithm-based SVM (SAPSO-SVM). In this experiment, serum samples from 95 patients with confirmed thyroid dysfunction and 90 serum samples from normal thyroid function were used for Raman spectroscopy. The experimental results show that the GAPSO-SVM algorithm has a high average diagnostic accuracy of 95.08% and has high sensitivity and specificity (91.67%, 97.96%). Compared with the traditional optimization algorithm, the algorithm has high diagnostic accuracy, short execution time, and good reliability. It can be seen that Raman spectroscopy combined with GAPSO-SVM diagnostic algorithm has enormous potential in noninvasive screening of thyroid dysfunction.

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