Title :
Machine Performance Degradation Assessment Based on PCA-FCMAC
Author :
Lei, Zhang ; Qixin, Cao
Author_Institution :
Res. Inst. of Robot., Shanghai Jiao Tong Univ., Shanghai
Abstract :
A PCA-FCMAC (Principal Component Analysis-Fuzzy Cerebellar Model Articulation Controller) model is proposed for machine performance degradation assessment. In the model, the selected features from the sensor signals are first processed by PCA to eliminate the redundant information and then inputted into FCMAC. FCMAC is used to assess degradation states quantitatively based on its local generalization ability. The implementation of the model is presented. Then the application in a drilling machine to assess the states of the cutting tool shows the effectiveness of the model. The comparative analyses of the assessing results prove FCMAC work better than CMAC.
Keywords :
fuzzy control; principal component analysis; fuzzy cerebellar model articulation controller; machine performance degradation assessment; principal component analysis; Cutting tools; Degradation; Drilling machines; Feature extraction; Machine intelligence; Performance analysis; Principal component analysis; Sensor phenomena and characterization; Spline; Temperature sensors; Degradation prediction; FCMAC; PCA;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.741