|Table of Contents|

[1] Zhang Lei, Cao Qixin, Jay Lee, Frank L. Lewis, et al. PCA-CMAC based machine performance degradation assessment [J]. Journal of Southeast University (English Edition), 2005, 21 (3): 299-303. [doi:10.3969/j.issn.1003-7985.2005.03.011]
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PCA-CMAC based machine performance degradation assessment()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
21
Issue:
2005 3
Page:
299-303
Research Field:
Mechanical Engineering
Publishing date:
2005-09-30

Info

Title:
PCA-CMAC based machine performance degradation assessment
Author(s):
Zhang Lei1 Cao Qixin1 Jay Lee2 Frank L. Lewis3
1Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200030, China
2 NSF I/UCR Center of Intelligent Maintenance Systems, University of Cincinnati, Cincinnati, OH 45221, USA
3Automation and Robotics Research Institute, University of Texas at Arlington, Arlington, TX 76019, USA
Keywords:
principal component analysis cerebellar model articulation controller(CMAC) performance degradation assessment
PACS:
TH17;TP18
DOI:
10.3969/j.issn.1003-7985.2005.03.011
Abstract:
A principal component analysis-cerebellar model articulation controller(PCA-CMAC)model is proposed for machine performance degradation assessment.PCA is used to feature selection, which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger, the generalization ability is better, but the difference of degradation states is not obvious.If r is smaller, the different states are distinct, but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.

References:

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Memo

Memo:
Biographies: Zhang Lei(1976—), female, graduate;Cao Qixin(corresponding author), male, doctor, professor, qxcao@sjtu.edu.cn.
Last Update: 2005-09-20