Dataset relaterat till processövervakning och tillståndsövervakning av en lagerringsslipmaskin Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder 2022-136-1-2 https://doi.org/10.5878/331q-3p13 Swedish National Data Service Svensk nationell datatjänst Landing page Dataset relaterat till processövervakning och tillståndsövervakning av en lagerringsslipmaskin Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder 2022-136-1-2 https://doi.org/10.5878/331q-3p13 oai:DiVA.org:ltu-92668 10.1007/s00170-022-09930-6 urn:nbn:se:ltu:diva-92668 oai:DiVA.org:ltu-90896 urn:nbn:se:ltu:diva-90896 10.1016/j.procir.2022.05.056 Ahmer, Muhammad Ahmer, Muhammad Manufacturing and Process Development, AB SKF Manufacturing and Process Development, AB SKF Department of Engineering Sciences and Mathematics, Machine Elements, Luleå University of Technology Institutionen för teknikvetenskap och matematik, Maskinelement, Luleå tekniska universitet Swedish National Data Service Svensk nationell datatjänst Landing page analysis analys grinding machines slipmaskiner diagnostics diagnostik maintenance underhåll (drift) bearings lager (maskinkomponenter) condition monitoring övervakning av skick 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". I publikationen (Ahmer, M., Sandin, F., Marklund, P. et al., 2022) har vi undersökt användningen av sensorer i en lagerringsslipmaskin för felklassificering och tillståndsövervakning. Föreslagen metod kombinerar domänkunskap om processövervakning och tillståndsövervakning för att framgångsrikt uppnå fellägesförutsägelse med hög noggrannhet med endast ett fåtal nyckelsensorer. Denna forskning visar att tillverkningsutrustning kan dra fördel av avancerad databehandling och maskininlärningsteknik. Slipmaskinen är av typ SGB55 från Lidköping Machine Tools och används i detta fall för att slipa löpbanor på lagerinnerringar av typ SKF-6210 spårkullager. Sensorer för vibration, akustisk emission, kraft och temperatur är installerade för att övervaka maskinens tillstånd under slipning och olika driftsförhållanden. Data insamlas från sensorerna samt maskinens numeriska styrenhet under drift. Utvalda producerade kvalitetsparametrar mäts efter slipoperationen. 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 Filerna är grupperade i mappar i zip-filer. Pdf-filen "readme_data_description.pdf" beskriver innehållet i filerna i mapparna. "lib" innehåller information om bibliotek som kan användas för att läsa .tdms-datafilerna i Matlab eller Python. Se den engelska beskrivningen för mer information. Numeric Raw time series data collected from machine and sensors during production of bearing rings and bearing rings quality measurement data.Raw time series data collected from machine and sensors during production of bearing rings and bearing rings quality measurement data. Rå tidsseriedata insamlad från maskin och sensorer under tillverkning av lagerringar och lagerringar kvalitetsmätdata.Rå tidsseriedata insamlad från maskin och sensorer under tillverkning av lagerringar och lagerringar kvalitetsmätdata. ExperimentExperiment ExperimentExperiment Access to data through SND. Data are freely accessible. Åtkomst till data via SND. Data är fritt tillgängliga. openAccess 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 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 10.1007/s00170-022-09930-6 urn:nbn:se:ltu:diva-92668 oai:DiVA.org:ltu-92668 2022 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 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:nbn:se:ltu:diva-90896 10.1016/j.procir.2022.05.056 oai:DiVA.org:ltu-90896 2022 oai:DiVA.org:ltu-90896