Fault Diagnosis Model Based on Support Vector Machine and Genetic Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 2535-2539 ◽  
Author(s):  
Wei Niu ◽  
Guo Qing Wang ◽  
Zheng Jun Zhai ◽  
Juan Cheng
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameters Optimization ◽  
Classification Model ◽  
Fault System ◽  
Support Vector ◽  
Diagnosis System ◽  
Intelligent Diagnosis ◽  
Model Based

Recently, the dominating difficulty that fault intelligent diagnosis system faces is terrible lack of typical fault samples, which badly prohibits the development of machinery fault intelligent diagnosis. Mainly according to the key problems of support vector machine need to resolve in fault intelligent diagnosis system, this paper makes more systemic and thorough researches in building fault classifiers, parameters optimization of kernel function. A decision directed acyclic graph fault diagnosis classification model based on parameters selected by genetic algorithm is proposed, abbreviated as GDDAG. Finally, GDDAG model is applied to rotor fault system, the testing results demonstrate that this model has very good classification precision and realizes the multi-faults diagnosis.

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhenhua Li ◽  
Junjie Cheng ◽  
A. Abu-Siada
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Fault Type ◽  
Svm Model ◽  
Transformer Winding

Background: Winding deformation is one of the most common faults that an operating power transformer experiences over its operational life. Thus it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding. A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a support vector machine (SVM) model for training. Parameters of the SVM model are optimized using a genetic algorithm (GA). Results : The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations. Conclusion : The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rate of the fault type and fault location are 100% and 90%, respectively.

scholarly journals Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1442 ◽  
Author(s):  
Tao Shen ◽  
Hong Yu ◽  
Yuan-Zhong Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Related Species ◽  
Predictive Accuracy ◽  
Classification Model ◽  
Venn Diagram ◽  
Support Vector ◽  
Stacked Generalization ◽  
Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis

2011 ◽  
Vol 216 ◽  
pp. 153-157
Author(s):  
D.L. Yang ◽  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Bearing ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

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