An effective and novel fault diagnosis technique based on EMD and SVM

Title:

An effective and novel fault diagnosis technique based on EMD and SVM

Creator:

Xian , Guang-ming and Zeng, Bi-qing

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9953b039-3571-43fe-b677-0e866b036bab
uuid:9953b039-3571-43fe-b677-0e866b036bab

Subject:

Fault diagnosis, empirical mode decomposition, support vector machine, intrinsic mode function, machinery, and RBF kernel function

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

An effective and novel roller bearing fault diagnosis technique based on empirical mode decomposition (EMD) energy entropy and support vector machine (SVM) is put forward in this article. The vibration signal of roller bearing is decomposed by EMD and the first 5 intrinsic mode function (IMF) components are obtained. SVM served as a fault diagnosis classifier and the extracted energy features of the first 5 IMFs are taken as network input vectors, and then the fault bearing and the normal bearing can be distinguished. An technique for fault of roller bearing by SVM is evaluated against a series of fault diagnosis methods that are widely used in machinery, with particular regard to the effect of training set size on fault diagnosis accuracy. We trained the SVM using RBF kernel function. We compare our experimental results with the existing results given by SMO and SVM-light algorithms. It can be seen that the fault diagnosis method based on SVM-light is superior to that based on SMO in diagnosis accuracy of roller bearing. In addition to the SVM, the same datasets were classified using RBF NN and Hopfield NN. The experimental results show that the technique of support vector machine based on EMD energy entropy has higher fault diagnosis ability.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:4

Harvested from:

CDK

Metadata only:

false