Paderborn Domain Generalization Version

Version 1.0
Ragab, Mohamed, 2022, "Paderborn Domain Generalization Version", https://doi.org/10.21979/N9/UCIK2K, DR-NTU (Data), V1
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Description
This dataset is generated the KAT data center in Paderborn University with the sampling rate of 64 KHz (Lessmeier et al. 2016). The damages were generated using both artificial and natural ways. More specifically, an electric discharge machine (EDM), a drilling, and an electric engraving were used to manually produce the artificial faults. While the natural damages were caused by using accelerated run-to-failure tests. The data collection process for both types of damages, i.e., artificial and real, was exposed under working conditions with different operating parameters such as loading torque, rotational speed and radial force. In total, the Paderborn datasets was collect under 6 different operating conditions including 3 conditions with artificial damages (denoted as domains I, J and K) and 3 conditions with real damages (denoted as domains L, M, and N). For example, the loading torque varies from 0.1 to 0.7 Nm and the radial force varies from 400 to 1000 N, while the rotational speed is fixed at 1500 RPM. Each operating condition (i.e., domain) contains three classes, namely, healthy class, inner fault (IF) class, and outer fault (OF) class. To prepare the data samples for the Paderborn dataset, we adopted sliding windows with a fixed length of 5,120 and a shifting size of 4,096 (Ragab et al. 2021). As such, we generated 12,340 for each artificial domain (i.e., I, J, and K) and 13,640 samples for each real domain (i.e., L, Mand N) respectively. (2022-03-06)
Subject Engineering
Keyword Domain Generalization, Fault Diagnosis
Related Publication Ragab, M., Chen, Z., Zhang, W., Eldele, E., Wu, M., Kwoh, C. K., & Li, X. (2022). Conditional Contrastive Domain Generalization for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2022.3154000
Grant Information
Agency for Science, Technology and Research (A*STAR): AME Programmatic Funds (Grant No. A20H6b0151)
Agency for Science, Technology and Research (A*STAR): Career Development Award (Grant No. C210112046)
License/Data Use Agreement

CC BY-NC 4.0

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paderborn/Unknown - 241.1 MB
Published Oct 7, 2022
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MD5: 6cfe98dab67cfae6f8b6e4fe1fe7a6db
artificial_to_real_settings.ipynb
data_preprocesing/Jupyter Notebook - 6.9 KB
Published Oct 7, 2022
74 Downloads
MD5: 79136b05a35d7d980a833b92219e4ed4
B.pt
paderborn/Unknown - 241.1 MB
Published Oct 7, 2022
93 Downloads
MD5: 35983d990bb237916aa6e202eb17a1b1
C.pt
paderborn/Unknown - 241.1 MB
Published Oct 7, 2022
87 Downloads
MD5: 7dab137dcfdd08b99261795fe021fc4b
createRollingWindow.m
data_preprocesing/preprocessing_scripts_matlab/old/MATLAB Source Code - 451 B
Published Oct 7, 2022
69 Downloads
MD5: 8f50d59e5cf5a45f3a7abda23b2b73c0
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paderborn/Unknown - 266.5 MB
Published Oct 7, 2022
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MD5: d61ba5ce2f358be96693fdfb47c480a7
data_extractiom_old.m
data_preprocesing/preprocessing_scripts_matlab/old/MATLAB Source Code - 2.1 KB
Published Oct 7, 2022
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MD5: b7179e0098038d06382466ca0b258d5b
Data_loading_segmentation.m
data_preprocesing/preprocessing_scripts_matlab/MATLAB Source Code - 3.3 KB
Published Oct 7, 2022
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MD5: ae326925af2468b84f31f40c7125d72b
domain_adaptation_settings.ipynb
data_preprocesing/Jupyter Notebook - 12.5 KB
Published Oct 7, 2022
72 Downloads
MD5: b7be7472d7ff8127c78b36471d68c1ef
domain_generalization_8_domains.ipynb
data_preprocesing/Jupyter Notebook - 13.1 KB
Published Oct 7, 2022
76 Downloads
MD5: 6ee1e25418aff08617c1039ebe9c6b2b
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