[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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[4] Zhang L, Cao Q X, Lee J.Credit-assignment CMAC algorithm for robust self-learning and self-maintenance machine [J].Tsinghua Science and Technology, 2004, 9(5):519-526.
[5] Yan J H, Muammer K.Predictive algorithm for machine degradation detection using logistic regression [A].In:5th International Conference on Managing Innovations in Manufacturing [C].Milwaukee, Wisconisn, USA, 2002.9-11.
[6] Dae S H, Lee J.An adaptive Watchdog for monitoring of machine degradation using ART2(adaptive resonance theory 2)[A].In:Proceedings of International Conference on Intelligent Maintenance Systems [C].Xi’an, China, 2003.806-810.
[7] Manabu K, Shinij H, Iori H, et al.A new multivariate statistical process monitoring method using principal component analysis [J].Computers and Chemical Engineering, 2001, 25(7, 8):1103-1113.
[8] Lewin D R.Predictive maintenance using PCA [J].Control Engineering Practice, 1995, 3(3):413-421.

Memo

Memo:

Biographies: Zhang Lei(1976—), female, graduate;Cao Qixin(corresponding author), male, doctor, professor, qxcao@sjtu.edu.cn.

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