Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning
AbstractThe 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
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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.
Huang Y-C, Kao C-H, Chen S-J. Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning. Applied Sciences. 2018; 8(7):1072.Chicago/Turabian Style
Huang, Yi-Cheng; Kao, Chi-Hsuan; Chen, Sheng-Jhe. 2018. "Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning." Appl. Sci. 8, no. 7: 1072.
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