Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/73838
Title: Sequential independent component analysis for cutting forces de-noising in micro-machining tool condition monitoring
Authors: Zhu, K. 
Hong, G.S. 
Wong, Y.S. 
Keywords: Force denoising
Independent component analysis
Kurtosis
Sequential extraction
Issue Date: 2004
Source: Zhu, K.,Hong, G.S.,Wong, Y.S. (2004). Sequential independent component analysis for cutting forces de-noising in micro-machining tool condition monitoring. ICMA 2004 - Proceedings of the International Conference on Manufacturing Automation: Advanced Design and Manufacturing in Global Competition : 833-840. ScholarBank@NUS Repository.
Abstract: In this paper, we applied a sequential independent component analysis (ICA) algorithm for cutting forces de-noising, as a preprocessor in micro milling condition monitoring. The sources can be extracted from the instantaneous mixtures (sensor outputs) with ICA based on kurtosis. We can extract the sources one by one with deflation until as expected with the algorithm. Such methods are attractive in micromachining monitoring since the expected cutting forces are contaminated by relatively large noises and the sources and noises are identified non-Gaussian. The results were illustrated in time domain. © Professional Engineering Publishing 2004.
Source Title: ICMA 2004 - Proceedings of the International Conference on Manufacturing Automation: Advanced Design and Manufacturing in Global Competition
URI: http://scholarbank.nus.edu.sg/handle/10635/73838
ISBN: 1860584683
Appears in Collections:Staff Publications

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