Details
| Original language | English |
|---|---|
| Pages (from-to) | 63-70 |
| Number of pages | 8 |
| Journal | CIRP Journal of Manufacturing Science and Technology |
| Volume | 33 |
| Early online date | 18 Mar 2021 |
| Publication status | Published - May 2021 |
Abstract
Unplanned machine downtimes lead to higher costs through reduced productivity. Machine availability is amongst others limited by the failure of wear-induced preload loss of ball screws. Therefore, attempts are made to monitor the preload. The high complexity and required robustness of preload monitoring leads to a low spreading in industry so far. This paper investigates the capability of sensor fusion based on principal component analysis to monitor preload loss of single nut ball screws. Features are investigated using different preload levels of the ball screw by selecting different ball-diameters. It is shown that this approach reliably detects the preload levels.
Keywords
- Ball screw, Condition monitoring, Multivariate feature analysis, Preload monitoring, Principal component analysis, Signal fusion
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: CIRP Journal of Manufacturing Science and Technology, Vol. 33, 05.2021, p. 63-70.
Research output: Contribution to journal › Book/Film/Article review in journal › Research
}
TY - JOUR
T1 - Preload monitoring of single nut ball screws based on sensor fusion
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Schmidt, Alexander
N1 - Funding Information: This work was supported by the Industrial Collective Research (“Industrielle Gemeinschaftsforschung” - IGF) program with the grant no. 19882 N of the Federal Ministry for Economic Affairs and Energy (“Bundesministerium für Wirtschaft und Energie” - BMWi), Germany. The authors would like to thank the Bosch Rexroth AG, Germany, for providing the ball screw drives.
PY - 2021/5
Y1 - 2021/5
N2 - Unplanned machine downtimes lead to higher costs through reduced productivity. Machine availability is amongst others limited by the failure of wear-induced preload loss of ball screws. Therefore, attempts are made to monitor the preload. The high complexity and required robustness of preload monitoring leads to a low spreading in industry so far. This paper investigates the capability of sensor fusion based on principal component analysis to monitor preload loss of single nut ball screws. Features are investigated using different preload levels of the ball screw by selecting different ball-diameters. It is shown that this approach reliably detects the preload levels.
AB - Unplanned machine downtimes lead to higher costs through reduced productivity. Machine availability is amongst others limited by the failure of wear-induced preload loss of ball screws. Therefore, attempts are made to monitor the preload. The high complexity and required robustness of preload monitoring leads to a low spreading in industry so far. This paper investigates the capability of sensor fusion based on principal component analysis to monitor preload loss of single nut ball screws. Features are investigated using different preload levels of the ball screw by selecting different ball-diameters. It is shown that this approach reliably detects the preload levels.
KW - Ball screw
KW - Condition monitoring
KW - Multivariate feature analysis
KW - Preload monitoring
KW - Principal component analysis
KW - Signal fusion
UR - http://www.scopus.com/inward/record.url?scp=85102644060&partnerID=8YFLogxK
U2 - 10.1016/j.cirpj.2021.02.006
DO - 10.1016/j.cirpj.2021.02.006
M3 - Book/Film/Article review in journal
AN - SCOPUS:85102644060
VL - 33
SP - 63
EP - 70
JO - CIRP Journal of Manufacturing Science and Technology
JF - CIRP Journal of Manufacturing Science and Technology
SN - 1755-5817
ER -