Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Bearing defects, Fault diagnosis, Fuzzy-neighborhood density-based clustering, Induction motors
In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.
Farajzadeh-Zanjani, M.; Razavi-Far, R.; Saif, M.; Zarei, J.; and Palade, V.. (2016). Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, 935-940.