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 language | English |
|---|---|
| Article number | 1072 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 8 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2018 Jun 30 |
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All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Instrumentation
- Engineering(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes
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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 journal › Article
TY - JOUR
T1 - Diagnosis of the hollow ball screw preload classification using machine learning
AU - Huang, Yi-Cheng
AU - Kao, Chi Hsuan
AU - Chen, Sheng Jhe
PY - 2018/6/30
Y1 - 2018/6/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049606754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049606754&partnerID=8YFLogxK
U2 - 10.3390/app8071072
DO - 10.3390/app8071072
M3 - Article
AN - SCOPUS:85049606754
VL - 8
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
SN - 2076-3417
IS - 7
M1 - 1072
ER -