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Appl. Sci. 2018, 8(7), 1072; https://doi.org/10.3390/app8071072

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

1
Department of Mechatronics Engineering, National Changhua University of Education, No. 1, Chinde Rd., Changhua city, Changhua 500, Taiwan
2
Precision Machinery Research and Development Center, Taichung 40768, Taiwan
*
Author to whom correspondence should be addressed.
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 27 June 2018 / Published: 30 June 2018
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Abstract

The prognostic diagnosis of machine-health status is an emerging research topic. In this study, the diagnostic results of hollow ball screws with various ball-nut preloads were obtained using a machine-learning approach. In this method, ball-screw pretension, oil circulation, and ball-nut preload were considered. A feature extraction was used to determine the hollow ball-screw preload status on the basis of vibration signals, servo-motor speed, servo-motor current signals, and linear scale counts. Preloads with 2%, 4%, and 6% ball screws were predesigned, manufactured, and operated. Signal patterns with various preload features, servo-motor speeds, servo-motor current signals, and linear scale counts were classified using the support vector machine (SVM) algorithm. The features of the vibration signal were classified using the genetic algorithm/k-nearest neighbor (GA/KNN) method. The complex and irregular model of the ball-screw-nut preload could be learned and supervised using the driving motion current, ball-screw speed, linear scale positioning, and vibration signals of the ball screw. The experimental results indicate that the prognostic status of the ball-nut preload can be determined using the proposed methodology. The proposed diagnostic method can be used to prognosticate the health status of the machine tool. View Full-Text
Keywords: ball screw; ball-nut preload; feature extraction; genetic algorithm; k-nearest neighbor; prognostic diagnosis; servo-motor current; support vector machine ball screw; ball-nut preload; feature extraction; genetic algorithm; k-nearest neighbor; prognostic diagnosis; servo-motor current; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Huang, Y.-C.; Kao, C.-H.; Chen, S.-J. Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning. Appl. Sci. 2018, 8, 1072.

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