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

SND-ID: 2022-136-1. Version: 1. DOI: https://doi.org/10.5878/s5fj-1x03

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Version 2: 2023-03-10

DOI: https://doi.org/10.5878/331q-3p13

Data corrected: One archive published in version 1 (test_5.zip) was corrupted. This is corrected and all .zip archives have been repacked in this version. Please note that the subfolders named dress_<number>/ in version 1 now are consistently named dresscyc_<number>/ in this version. Some metadata entries in the .tdms file headers were modified in the process.

Citation

Research principal

Luleå University of Technology - Department of Engineering Sciences and Mathematics rorId

Description

In the article (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., & Berg

... Show more..
In the article (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., & Berglund, K. (2022). Failure mode classification for condition-based maintenance in a bearing ring grinding machine. In The International Journal of Advanced Manufacturing Technology (Vol. 122, pp. 1479–1495). https://doi.org/10.1007/s00170-022-09930-6

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 includes .tdms files for fifteen rings for their individual grinding cycle. The column description for both "Analogue" and "Digital" channels are described in the "readme_data_description.pdf" file.
The machine and process parameters used for the tests as sampled from the machine's control system (Numerical Controller) and compiled for all test runs in a single file "process_data.csv" in the folder "proc_param". The column description is available in "readme_data_description.pdf" under "Process Parameters".
The measured quality data (nine quality parameters - normalized) of the selected produced parts are recorded in the file "measured_quality_param.csv" under folder "quality". The description of the quality parameters is available in "readme_data_description.pdf".
The quality parameter disposition based on their actual acceptance tolerances for the process step is presented in file "quality_disposition.csv" under folder "quality". Show less..

Data contains personal data

No

Language

Method and outcome

Data format / data structure

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
Geographic coverage
Administrative information

Responsible department/unit

Department of Engineering Sciences and Mathematics

Topic and keywords

Research area

Other electrical engineering, electronic engineering, information engineering (Standard för svensk indelning av forskningsämnen 2011)

Reliability and maintenance (Standard för svensk indelning av forskningsämnen 2011)

Publications

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Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & Berglund, K. (2022). Failure mode classification for condition-based maintenance in a bearing ring grinding machine. In The International Journal of Advanced Manufacturing Technology (Vol. 122, pp. 1479–1495). 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
SwePub: oai:DiVA.org:ltu-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
SwePub: oai:DiVA.org:ltu-90896

If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.

License

CC BY 4.0

Versions

Version 2. 2023-03-10

Version 2: 2023-03-10

DOI: https://doi.org/10.5878/331q-3p13

Data corrected: One archive published in version 1 (test_5.zip) was corrupted. This is corrected and all .zip archives have been repacked in this version. Please note that the subfolders named dress_<number>/ in version 1 now are consistently named dresscyc_<number>/ in this version. Some metadata entries in the .tdms file headers were modified in the process.

Version 1. 2022-09-07

Version 1: 2022-09-07

DOI: https://doi.org/10.5878/s5fj-1x03

Contact for questions about the data

Published: 2022-09-07
Last updated: 2023-03-10