Fault Signal Classification using Adaptive Boosting Algorithm

  • Pei Yao Northwestern Polytechnical University
  • Zhenbao Liu Northwestern Polytechnical University
  • Zhongsheng Wang Northwestern Polytechnical University
  • Shuhui Bu Northwestern Polytechnical University
Keywords: Classification algorithms, decision trees, fault diagnosis, feature extraction


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.

DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2635

How to Cite
Yao, P., Liu, Z., Wang, Z., & Bu, S. (2012). Fault Signal Classification using Adaptive Boosting Algorithm. Elektronika Ir Elektrotechnika, 18(8), 97-100. https://doi.org/10.5755/j01.eee.18.8.2635