Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - An approach for bearing fault diagnosis based on PCA and multiple classifier fusion
AU - Xia, Min
AU - Kong, Fanrang
AU - Hu, Fei
PY - 2011/10/24
Y1 - 2011/10/24
N2 - The purpose of this paper is to propose a new system, with both high efficiency and accuracy for fault diagnosis of rolling bearing. After pretreatment and choosing sensitive features of different working conditions of bearing from both time and frequency domain, principal component analysis(PCA) is conducted to compress the data dimension and eliminate the correlation among different statistical features. The first several principal components are sent to the classifier for recognition. However, recognition method with a single classifier usually has only a limited classification capability that is insufficient for real applications. An ongoing strategy is the decision fusion techniques. The system proposed in this paper develops a decision fusion algorithm for fault diagnosis, which integrates decisions of multiple classifiers. First, the front four principle components are chosen as input of individual classifier. A selection process of the classifiers is then operated on the basis of correlation measure for the purpose of finding an optimal sequence of them. Finally, classifier fusion algorithm based on Bayesian belief method is applied to generate the final decision. The result of experiments show that this new bearing fault diagnosis system recognize different working conditions of bearing more accurately and more stably than a single classifier does, which demonstrates the high efficiency of the proposed system.
AB - The purpose of this paper is to propose a new system, with both high efficiency and accuracy for fault diagnosis of rolling bearing. After pretreatment and choosing sensitive features of different working conditions of bearing from both time and frequency domain, principal component analysis(PCA) is conducted to compress the data dimension and eliminate the correlation among different statistical features. The first several principal components are sent to the classifier for recognition. However, recognition method with a single classifier usually has only a limited classification capability that is insufficient for real applications. An ongoing strategy is the decision fusion techniques. The system proposed in this paper develops a decision fusion algorithm for fault diagnosis, which integrates decisions of multiple classifiers. First, the front four principle components are chosen as input of individual classifier. A selection process of the classifiers is then operated on the basis of correlation measure for the purpose of finding an optimal sequence of them. Finally, classifier fusion algorithm based on Bayesian belief method is applied to generate the final decision. The result of experiments show that this new bearing fault diagnosis system recognize different working conditions of bearing more accurately and more stably than a single classifier does, which demonstrates the high efficiency of the proposed system.
KW - fault diagnosis
KW - multiple classifier fusion
KW - PCA
KW - rolling bearing
U2 - 10.1109/ITAIC.2011.6030215
DO - 10.1109/ITAIC.2011.6030215
M3 - Conference contribution/Paper
AN - SCOPUS:80054753579
SN - 9781424486236
T3 - Proceedings - 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2011
SP - 321
EP - 325
BT - Proceedings - 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2011
PB - IEEE
T2 - 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2011
Y2 - 20 August 2011 through 22 August 2011
ER -