Features extracted from real world applications increase dramatically, while machine learning methods decrease their performance given the previous scenario, and feature reduction is required. Particularly, for fault diagnosis in rotating machinery, the number of extracted features are sizable in order to collect all the available information from several monitored signals. Several approaches lead to data reduction using supervised or unsupervised strategies, where the supervised ones are the most reliable and its main disadvantage is the beforehand knowledge of the fault condition. This work proposes a new unsupervised algorithm for feature selection based on attribute clustering and rough set theory. Rough set theory is used to compute similarities between features through the relative dependency. The clustering approach combines classification based on distance with clustering based on prototype to group similar features, without requiring the number of clusters as an input. Additionally, the algorithm has an evolving property that allows the dynamic adjustment of the cluster structure during the clustering process, even when a new set of attributes feeds the algorithm. That gives to the algorithm an incremental learning property, avoiding a retraining process. These properties define the main contribution and significance of the proposed algorithm. Two fault diagnosis problems of fault severity classification in gears and bearings are studied to test the algorithm. Classification results show that the proposed algorithm is able to select adequate features as accurate as other feature selection and reduction approaches.
Bibliographical noteFunding Information:
The work was sponsored in part by the GIDTEC project No. 017-007-2015-11-05, and the Prometeo Project of the Secretariat for High Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador. The experimental work was developed at the Vibration Laboratory of GIDTEC, in the Universidad Politécnica Salesiana de Cuenca-Ecuador.
© 2016 Elsevier Ltd
- Attribute clustering
- Fault severity classification
- Feature selection
- Rotating machinery
- Rough set