Research and Application of Animal Disease Intelligent Diagnosis Based on Support Vector Machine

2009 ◽  
Author(s):  
Long Wan ◽  
Wenxing Bao
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Animal Disease ◽  
Intelligent 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.

scholarly journals Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Hongtao Xue ◽  
Zhongxing Li ◽  
Huaqing Wang ◽  
Peng Chen
Keyword(s):  
Support Vector Machine ◽  
Centrifugal Pump ◽  
Early Stage ◽  
Possibility Theory ◽  
Support Vector ◽  
Optimum Frequency ◽  
Pump System ◽  
Vibration Signals ◽  
Intelligent Diagnosis ◽  
Diagnosis Method

This paper proposed an intelligent diagnosis method for a centrifugal pump system using statistic filter, support vector machine (SVM), possibility theory, and Dempster-Shafer theory (DST) on the basis of the vibration signals, to diagnose frequent faults in the centrifugal pump at an early stage, such as cavitation, impeller unbalance, and shaft misalignment. Firstly, statistic filter is used to extract the feature signals of pump faults from the measured vibration signals across an optimum frequency region, and nondimensional symptom parameters (NSPs) are defined to represent the feature signals for distinguishing fault types. Secondly, the optimal classification hyperplane for distinguishing two states is obtained by SVM and NSPs, and its function is defined as synthetic symptom parameter (SSP) in order to increase the diagnosis’ sensitivity. Finally, the possibility functions of the SSP are used to construct a sequential fuzzy diagnosis for fault detection and fault-type identification by possibility theory and DST. The proposed method has been applied to detect the faults of the centrifugal pump, and the efficiency of the method has been verified using practical examples.

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

2017 ◽  
Vol 40 (8) ◽  
pp. 2681-2693 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Qi Zhao ◽  
Lei Wang ◽  
Kai Yin
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Rotating Machinery ◽  
Support Vector ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Intelligent Diagnosis

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.

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.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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