Creator/Principal investigator(s)
Muhammad Ahmer
- AB SKF, Manufacturing and Process Development
Pär Marklund
- Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements
Fredrik Sandin
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab
Martin Gustafsson - AB SKF, Manufacturing and Process Development
Kim Berglund
- Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements
Description
In the manuscript, (Ahmer, M., Sandin, F., Marklund, P. et al., 2022) we have investigated the effective use of sensors in a bearing ring grinder for failure classification in the condition-based maintenance context. The proposed methodology combines domain knowledge of process monitoring and condition monitoring to successfully achieve failure mode prediction with high accuracy using only a few key sensors. This enables manufacturing equipment to take advantage of advanced data processing and machine learning techniques.
The grinding machine is of type SGB55 from Lidköping Machine Tools and is used to produce functional raceway surface of inner rings of type SKF-6210 deep groove ball bearing. Additional sensors like vibration, acoustic emission, force, and temperature sensors are installed to monitor machine condition while producing bearing components under different operating conditions. Data is sampled from sensors as well as the machine's numerical controller during operation. Selected parts are measured for the produced quality.
Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & B
Language
English
Research principal
Responsible department/unit
Department of Engineering Sciences and Mathematics, Machine Elements.
Data contains personal data
No
Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & Berglund, K. (n.d.). Failure mode classification for condition-based maintenance in a bearing ring grinding machine. In The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-022-09930-6
DOI:
https://doi.org/10.1007/s00170-022-09930-6
URN:
urn:nbn:se:ltu:diva-92668
Ahmer, M., Marklund, P., Gustafsson, M., & Berglund, K. (2022). An implementation framework for condition-based maintenance in a bearing ring grinder. In Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022 (pp. 746–751). https://doi.org/10.1016/j.procir.2022.05.056
URN:
urn:nbn:se:ltu:diva-90896
DOI:
https://doi.org/10.1016/j.procir.2022.05.056
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Description
The files are of three categories and are grouped in zipped folders. The pdf file named "readme_data_description.pdf" describes the content of the files in the folders. The "lib" includes the information on libraries to read the .tdms Data Files in Matlab or Python.
The raw time-domain sensors signal data are grouped in seven main folders named after each test run e.g. "test_1"... "test_7". Each test includes seven dressing cycles named e.g. "dresscyc_1"... "dresscyc_7". Each dressing cycle inc
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https://doi.org/10.5878/s5fj-1x03
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Creator/Principal investigator(s)
Muhammad Ahmer
- AB SKF, Manufacturing and Process Development
AB SKF, Manufacturing and Process Development
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements
Keywords
analysis, grinding machines, diagnostics, maintenance, bearings, condition monitoring
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