Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Motorized spindle is the critical component in the CNC machine tool, and it is of considerable significance to obtain its overall performance and expose its weak link via conducting reliability test. Multi-dimensional signals with high sampling frequency and long storage period pose a great challenge to data collection and management process. This paper is devoted to proposing an efficient and systematic method for data acquisition, feature extraction, and data management methods. The redundancy of the original signal is removed by similarity analysis of feature matrixes, after which the most typical sample during the test is preserved as a data sample for data mining and data analysis. The proposed strategy can not only be applied in the motorized spindle but also be a guide for the test of similar mechanical system.
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