Diagnosis of the hollow ball screw preload classification using machine learning

Yi-Cheng Huang, Chi Hsuan Kao, Sheng Jhe Chen

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Article number1072
JournalApplied Sciences (Switzerland)
Volume8
Issue number7
DOIs
Publication statusPublished - 2018 Jun 30

Fingerprint

Ball screws
machine learning
screws
Learning systems
balls
hollow
Health
vibration
health
Machine tools
machine tools
Support vector machines
Feature extraction
Oils
Genetic algorithms
genetic algorithms
pattern recognition
positioning
emerging
oils

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

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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.",
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Diagnosis of the hollow ball screw preload classification using machine learning. / Huang, Yi-Cheng; Kao, Chi Hsuan; Chen, Sheng Jhe.

In: Applied Sciences (Switzerland), Vol. 8, No. 7, 1072, 30.06.2018.

Research output: Contribution to journalArticle

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