<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>CWRU Domain Generalization Version</dcterms:title><dcterms:identifier>https://doi.org/10.21979/N9/8QPQHL</dcterms:identifier><dcterms:creator>Ragab, Mohamed</dcterms:creator><dcterms:publisher>DR-NTU (Data)</dcterms:publisher><dcterms:issued>2023-04-03</dcterms:issued><dcterms:modified>2023-04-03T02:10:02Z</dcterms:modified><dcterms:description>The 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.</dcterms:description><dcterms:subject>Engineering</dcterms:subject><dcterms:subject>Domain Generalization, Fault Diagnosis</dcterms:subject><dcterms:isReferencedBy>Ragab, M., Chen, Z., Zhang, W., Eldele, E., Wu, M., Kwoh, C. K., &amp; Li, X. (2022). Conditional Contrastive Domain Generalization for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 71, 3506912-., doi, 10.1109/TIM.2022.3154000, https://ieeexplore.ieee.org/abstract/document/9721021</dcterms:isReferencedBy><dcterms:isReferencedBy>Ragab, M., Chen, Z., Zhang, W., Eldele, E., Wu, M., Kwoh, C. K. &amp; Li, X. (2022). Conditional contrastive domain generalization for fault diagnosis. IEEE Transactions On Instrumentation and Measurement, 71, 3506912-., handle, 10356/163780, https://hdl.handle.net/10356/163780</dcterms:isReferencedBy><dcterms:contributor>Ragab, Mohamed</dcterms:contributor><dcterms:dateSubmitted>2022-03-06</dcterms:dateSubmitted><dcterms:type>Time series</dcterms:type><dcterms:license>CC BY-NC 4.0</dcterms:license></metadata>