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.
|Pages||935 - 940|
|Publication status||Published - 2015|
|Event||2015 IEEE 14th International Conference on Machine Learning and Applications - Miami, United States|
Duration: 9 Dec 2015 → 11 Dec 2015
|Conference||2015 IEEE 14th International Conference on Machine Learning and Applications|
|Period||9/12/15 → 11/12/15|
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- bearing defects
- fault diagnosis
- fuzzy-neighborhood density-based clustering
- induction motors