Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder

SND-ID: 2022-136

Creator/Principal investigator(s)

Muhammad Ahmer - AB SKF, Manufacturing and Process Development orcid

Pär Marklund - Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements orcid

Fredrik Sandin - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab orcid

Martin Gustafsson - AB SKF, Manufacturing and Process Development

Kim Berglund - Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements orcid

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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

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Language

English

Research principal, contributors, and funding

Research principal

Luleå University of Technology

Responsible department/unit

Department of Engineering Sciences and Mathematics, Machine Elements.

Protection and ethical review

Data contains personal data

No

Method and time period
Geographic coverage
Publications

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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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Dataset
Dataset for the Implementation of Condition-based Maintenance and Maintenance Decision-making of a Bearing Ring Grinder

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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Version 1

Citation

Muhammad Ahmer, AB SKF, Manufacturing and Process Development, Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements (2022). Dataset for the Implementation of Condition-based Maintenance and Maintenance Decision-making of a Bearing Ring Grinder. Swedish National Data Service. Version 1. https://doi.org/10.5878/s5fj-1x03

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Data format / data structure

Numeric

Data collection

  • Mode of collection: Experiment
  • Description of the mode of collection: Raw time series data collected from machine and sensors during production of bearing rings and bearing rings quality measurement data.
  • Data collector: AB SKF
  • Instrument: Lidköping SGB55 - External Grinding machine used in SKF for bearing ring grinding
  • Source of the data: Physical objects

License

Creative Commons  Attribution 4.0 International (CC BY 4.0)
Published: 2022-09-07
Last updated: 2022-09-07