Details
| Original language | English |
|---|---|
| Pages (from-to) | 270-275 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 102 |
| Early online date | 27 Sept 2021 |
| Publication status | Published - 2021 |
| Event | 18th CIRP Conference on Modeling of Machining Operations, CMMO 2021 - Ljubljana, Slovenia Duration: 15 Jun 2021 → 17 Jun 2021 |
Abstract
The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample.
Keywords
- condition monitoring, data compression, differential pulse-code modulation, health monitoring, machine tools
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 102, 2021, p. 270-275.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Analysis of the impact of data compression on condition monitoring algorithms for ball screws
AU - Hinrichs, Reemt
AU - Schmidt, Alexander
AU - Koslowski, Julian
AU - Bergmann, Benjamin
AU - Denkena, Berend
AU - Ostermann, Jörn
N1 - Funding Information: The work of ”WiZuBe” was supported by the Industrial Community Research program IGF (Industrielle Gemein-schaftsforschung) with the grant no. 19882 N of the Federal Ministry for Economic Affairs and Energy (Bundesministerium fr Wirtschaft und Energie, BMWi).
PY - 2021
Y1 - 2021
N2 - The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample.
AB - The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample.
KW - condition monitoring
KW - data compression
KW - differential pulse-code modulation
KW - health monitoring
KW - machine tools
UR - http://www.scopus.com/inward/record.url?scp=85116919461&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2021.09.046
DO - 10.1016/j.procir.2021.09.046
M3 - Conference article
AN - SCOPUS:85116919461
VL - 102
SP - 270
EP - 275
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 18th CIRP Conference on Modeling of Machining Operations, CMMO 2021
Y2 - 15 June 2021 through 17 June 2021
ER -