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A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction
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A hybrid Prognostics Approach for Motorized Spindle-Tool Holder Remaining Useful Life V0.1.pdf | Accepted version | 669.1 kB | Adobe PDF | View/Open |
Title: | A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction |
Authors: | Han, F Wang, H Qiu, C Xu, Y |
Item Type: | Conference Paper |
Abstract: | The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer. |
Issue Date: | 28-Aug-2020 |
Date of Acceptance: | 1-Aug-2020 |
URI: | http://hdl.handle.net/10044/1/97397 |
DOI: | 10.1007/978-3-030-57745-2_114 |
ISBN: | 9783030577445 |
ISSN: | 2190-3018 |
Publisher: | Springer International Publishing |
Start Page: | 1385 |
End Page: | 1400 |
Copyright Statement: | © 2020 Springer Nature Switzerland AG. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-57745-2_114 |
Conference Name: | Advances in Asset Management and Condition Monitoring - COMADEM 2019 |
Publication Status: | Published |
Start Date: | 2019-09 |
Finish Date: | 2019-09 |
Conference Place: | University of Huddersfield, UK |
Online Publication Date: | 2020-08-28 |
Appears in Collections: | Mechanical Engineering |