Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder
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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".
本研究依托Ahmer、M.、Sandin、F.、Marklund、P.等（2022）的研究工作，针对轴承环磨床在状态维修（condition-based maintenance, CBM）场景下的故障分类任务，探究了传感器的高效应用方案。所提出的方法融合了过程监控与状态监控领域的专业知识，仅依靠少量关键传感器即可实现高精度的故障模式预测，可助力制造设备充分利用先进数据处理与机器学习技术。 本次实验所用磨床为Lidköping机床厂生产的SGB55型磨床，用于加工SKF-6210型深沟球轴承内圈的功能性滚道表面。实验中加装了振动、声发射（acoustic emission, AE）、力与温度传感器，以监测不同工况下轴承零件加工过程中的机床状态。实验过程中，同步采集传感器数据与机床数控系统（Numerical Controller, NC）的采样数据，并对选定加工零件的成品质量进行检测。 Ahmer M, Sandin F, Marklund P, Gustafsson M, Berglund K. (2022). 轴承环磨床状态维修的故障模式分类[J]. 国际先进制造技术期刊, 第122卷, 第1479-1495页. https://doi.org/10.1007/s00170-022-09930-6 数据集分为三类，打包为压缩文件夹。名为"readme_data_description.pdf"的PDF文件对各文件夹内的文件内容进行了详细说明。"lib"文件夹包含了用于在Matlab或Python中读取.tdms格式数据文件的库使用说明。 原始时域传感器信号数据分为7个主文件夹，以各测试运行命名，例如"test_1"至"test_7"。每个测试包含7个砂轮修整循环，命名如"dresscyc_1"至"dresscyc_7"。每个修整循环下包含15个轴承环的.tdms格式数据文件，对应各自的磨削加工循环。模拟量（Analogue）与数字量（Digital）通道的列说明均在"readme_data_description.pdf"文件中予以详述。 实验所用机床与加工参数由机床数控系统采集，并汇总为单个文件"process_data.csv"，存放于"proc_param"文件夹中，涵盖所有测试运行的参数数据。参数列说明可在"readme_data_description.pdf"的"加工参数"板块中查阅。 选定加工零件的实测质量数据（包含9项归一化质量参数）存储于"quality"文件夹下的"measured_quality_param.csv"文件中，质量参数的说明详见"readme_data_description.pdf"文件。 基于该加工工序实际验收公差的质量参数配置情况，存储于"quality"文件夹下的"quality_disposition.csv"文件中。
