CWRU Domain Generalization Versiondoi:10.21979/N9/8QPQHLDR-NTU (Data)2023-04-031Ragab, Mohamed, 2023, "CWRU Domain Generalization Version", https://doi.org/10.21979/N9/8QPQHL, DR-NTU (Data), V1CWRU Domain Generalization Versiondoi:10.21979/N9/8QPQHLRagab, MohamedPythonAME Programmatic Funds (Grant No. A20H6b0151)Career Development Award (Grant No. C210112046)DR-NTU (Data)Ragab, MohamedRagab, Mohamed2022-03-06EngineeringDomain Generalization, Fault DiagnosisThe Case Western Reserve University (CWRU) is a widely adopted dataset for rolling bearing elements (Smith and Randall 2015). Accelerometer sensors were deployed at both the drive-end and fan-end of the housing motors. Vibration signals were collected with 12 KHz sampling rate under eight different operating conditions. Particularly, we have four different operating conditions with different loading torques collected from the drive end, denoted as domain A, B, C, and D. Similarly, we have other four operating conditions collected from the fan-end of the motor, denoted as domain F, G, H, and I. For each operating condition, there is one healthy state and three faulty states, namely, inner fault (IF), outer fault (OF), and bearing fault (BF). Each faulty state has three levels of severity with dimensions of 7, 14, 21 mil. In total, we have 10 classes with 1 healthy class and 9 faulty classes. To prepare the data for our experiments, we partitioned the sensor readings into smaller samples using sliding windows with a fixed length of 4,096 and the shifting step of 290, which is widely used in previous studies (Zhang et al. 2017; Ragab et al. 2021). Overall, we can generate 4,000 samples for each domain.Time seriesRagab, 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, 71, 3506912-.10.1109/TIM.2022.3154000Ragab, 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, 71, 3506912-.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, 71, 3506912-.10356/163780Ragab, 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, 71, 3506912-.dg_settings_prerocessing.ipynbapplication/x-ipynb+jsonA.ptapplication/octet-streamB.ptapplication/octet-streamC.ptapplication/octet-streamD.ptapplication/octet-streamE.ptapplication/octet-streamF.ptapplication/octet-streamG.ptapplication/octet-streamH.ptapplication/octet-stream