TY - CONF
T1 - GKFP: A new fuzzy clustering method applied to bearings diagnosis
AU - Li, Chuan
AU - Cerrada, Mariela
AU - Sánchez, René Vinicio
AU - Cabrera, Diego
AU - Ledo, Luiz
AU - Delgado, Myriam
AU - De Oliveira, José Valente
PY - 2019/1/4
Y1 - 2019/1/4
N2 - This paper proposes a new clustering method called Gustafson-Kessel with Focal Point (GKFP). The proposal aims at benefiting from the advantage of using Gustafson-Kessel clustering technique leveraged by the use of a Focal Point which enables obtaining partitions with different levels of granularity. Thus the method identifies clusters with uncorrelated or strongly correlated data while it allows the user to explore different regions of the feature space with different levels of detail. Due to the possibility of dealing with correlated data, a regularization procedure might be necessary. Therefore, the paper also briefly describes a Bayesian regularization which can be associated with GKFP. Experiments from bearing fault diagnosis show that GKFP outperforms three other clustering techniques, i.e., the popular fuzzy c-means (FCM), Gustafson-Kessel (GK), and the state of the art FCMFP, for two different bearing data sets.
AB - This paper proposes a new clustering method called Gustafson-Kessel with Focal Point (GKFP). The proposal aims at benefiting from the advantage of using Gustafson-Kessel clustering technique leveraged by the use of a Focal Point which enables obtaining partitions with different levels of granularity. Thus the method identifies clusters with uncorrelated or strongly correlated data while it allows the user to explore different regions of the feature space with different levels of detail. Due to the possibility of dealing with correlated data, a regularization procedure might be necessary. Therefore, the paper also briefly describes a Bayesian regularization which can be associated with GKFP. Experiments from bearing fault diagnosis show that GKFP outperforms three other clustering techniques, i.e., the popular fuzzy c-means (FCM), Gustafson-Kessel (GK), and the state of the art FCMFP, for two different bearing data sets.
KW - Bayes Inference
KW - Clustering
KW - Fuzzy covariance matrix
KW - GKFP
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85061811811&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85061811811&origin=inward
UR - http://www.mendeley.com/research/gkfp-new-fuzzy-clustering-method-applied-bearings-diagnosis
U2 - 10.1109/PHM-Chongqing.2018.00227
DO - 10.1109/PHM-Chongqing.2018.00227
M3 - Paper
SP - 1295
EP - 1300
T2 - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Y2 - 4 January 2019
ER -