TY  - JOUR
AU  - Yao, Pei
AU  - Liu, Zhenbao
AU  - Wang, Zhongsheng
AU  - Bu, Shuhui
PY  - 2012/10/17
Y2  - 2025/01/02
TI  - Fault Signal Classification using Adaptive Boosting Algorithm
JF  - Elektronika ir Elektrotechnika
JA  - ELEKTRON ELEKTROTECH
VL  - 18
IS  - 8
SE  - 
DO  - 10.5755/j01.eee.18.8.2635
UR  - https://eejournal.ktu.lt/index.php/elt/article/view/2635
SP  - 97-100
AB  - In recent years, researchers seldom investigate how to boost the classification performance of any learning algorithm for fault signal detection. We propose a fault signal classification method based on adaptive boosting (adaboost) in this paper. Adaboost is able to select an optimal linear combination of classifiers to form an ensemble whose joint decision rule has relatively high accuracy on the training set. First, we extract statistical features from sample signals. And then we make use of a decision tree to identify optimal features, which are used to classify the sample set by adaboost algorithm. To verify its accuracy, we set up the roller bearing experiment. Practical results show that the method can precisely identify fault signals, and be comparable to SVM based traditional method.<p>DOI: <a href="http://dx.doi.org/10.5755/j01.eee.18.8.2635">http://dx.doi.org/10.5755/j01.eee.18.8.2635</a></p>
ER  -