Please use this identifier to cite or link to this item: https://doi.org/10.1109/TII.2009.2023318
Title: Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification
Authors: Zhou, J.-H.
Pang, C.K. 
Lewis, F.L.
Zhong, Z.-W.
Keywords: Least square error (LSE)
Principal component analysis (PCA)
Principal feature analysis (PFA)
Recursive least squares (RLS)
Singular value decomposition (SVD)
Issue Date: Nov-2009
Source: Zhou, J.-H.,Pang, C.K.,Lewis, F.L.,Zhong, Z.-W. (2009-11). Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification. IEEE Transactions on Industrial Informatics 5 (4) : 454-464. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2009.2023318
Abstract: Identification and prediction of a lifetime of industrial cutting tools using minimal sensors is crucial to reduce production costs and downtime in engineering systems. In this paper, we provide a formal decision software tool to extract the dominant features enabling tool wear prediction. This decision tool is based on a formal mathematical approach that selects dominant features using the singular value decomposition of real-time measurements from the sensors of an industrial cutting tool. Selection of dominant features is important, as retaining only essential features allows reduced signal processing or even reduction in the number of required sensors, which cuts costs. It is shown that the proposed method of dominant feature selection is optimal in the sense that it minimizes the least-squares estimation error. The identified dominant features are used with the recursive least squares (RLS) algorithm to identify parameters in forecasting the time series of cutting tool wear. Experimental results on an industrial high-speed milling machine show the effectiveness in predicting the tool wear using only the dominant features. © 2009 IEEE.
Source Title: IEEE Transactions on Industrial Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/56357
ISSN: 15513203
DOI: 10.1109/TII.2009.2023318
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