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|Title:||Cutting force denoising in micro-milling tool condition monitoring||Authors:||Zhu, K.
Independent component analysis (ICA)
|Issue Date:||Aug-2008||Citation:||Zhu, K., Hong, G.S., Wong, Y.S., Wang, W. (2008-08). Cutting force denoising in micro-milling tool condition monitoring. International Journal of Production Research 46 (16) : 4391-4408. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540500469610||Abstract:||An independent component analysis (ICA) algorithm for cutting force denoising was applied in micro-milling tool condition monitoring. In micro-milling, the comparatively small cutting force signal is prone to contamination by relatively large noise, and as a result it is important to denoise the force signal before further processing it. However, the traditional denoising methods, based on Gaussian noise assumption, lose here because the noise is identified as containing a high non-Gaussian component in the experiment. ICA was recently developed to deal with the blind source separation (BSS) problem. It solves the BSS problem by measuring the non-Gaussianity of the signal and it is particularly effective in the separation of non-Gaussian signals. This approach employs fixed-point ICA (FastICA), assuming the noises are sources and the force signal is an instantaneous mixture of sources and by treating the signal denoising process as a BSS. The results are illustrated both in time and frequency domains. The FastICA denoising performances are compared with the popular wavelet thresholding. The results show that FastICA performs better than wavelet. Theoretical discussion of the nature of ICA and wavelet thresholding supports the results: ICA separates both Gaussian and non-Gaussian noise sources, while wavelet only suppresses Gaussian noise.||Source Title:||International Journal of Production Research||URI:||http://scholarbank.nus.edu.sg/handle/10635/59827||ISSN:||00207543||DOI:||10.1080/00207540500469610|
|Appears in Collections:||Staff Publications|
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