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Experimental derivation of a condition monitoring test cycle for machine tool feed drives
- Source :
- Production Engineering. 16:55-64
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Due to their critical influence on manufacturing accuracy, machine tool feed drives and the monitoring of their condition has been a research field of increasing interest for several years already. Accurate and reliable estimates of the current condition of the machine tool feed drive’s components ball screw drive (BSD) and linear guide shoes (LGSs) are expected to significantly enhance the maintainability of machine tools, which finally leads to economic benefits and smoother production. Therefore, many authors performed extensive experiments with different sensor signals, features and components. Most of those experiments were performed on simplified test benches in order to gain genuine and distinct insights into the correlations between the recorded sensor signals and the investigated fault modes. However, in order to build the bridge between real use cases and scientific findings, those investigations have to be transferred and performed on a more complex test bench, which is close to machine tools in operation. In this paper, a condition monitoring test cycle is developed for such a test bench. The developed test cycle enables the recording of a re-producible data basis, on which models for the condition monitoring of BSDs and LGSs can be based upon.
- Subjects :
- 0209 industrial biotechnology
Test bench
business.product_category
Computer science
Mechanical Engineering
Maintainability
Condition monitoring
Control engineering
02 engineering and technology
Fault (power engineering)
Industrial and Manufacturing Engineering
Field (computer science)
Bridge (nautical)
ddc
Machine Tool
Machine tool
Feed drive
Test cycle
020303 mechanical engineering & transports
020901 industrial engineering & automation
0203 mechanical engineering
Use case
business
Subjects
Details
- ISSN :
- 18637353 and 09446524
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- Production Engineering
- Accession number :
- edsair.doi.dedup.....bf37cf83e7890a1cfde916c3059d5904