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Part 1: Document Description
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Citation |
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Title: |
Paderborn Dataset Processed for Domain Adaptation Scenarios |
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Identification Number: |
doi:10.21979/N9/X6M827 |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2022-06-16 |
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Version: |
1 |
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Bibliographic Citation: |
Ragab, Mohamed, 2022, "Paderborn Dataset Processed for Domain Adaptation Scenarios", https://doi.org/10.21979/N9/X6M827, DR-NTU (Data), V1 |
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Citation |
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Title: |
Paderborn Dataset Processed for Domain Adaptation Scenarios |
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Identification Number: |
doi:10.21979/N9/X6M827 |
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Authoring Entity: |
Ragab, Mohamed (Nanyang Technological University) |
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Software used in Production: |
Python |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Ragab, Mohamed |
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Depositor: |
Ragab, Mohamed |
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Date of Deposit: |
2022-03-06 |
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Holdings Information: |
https://doi.org/10.21979/N9/X6M827 |
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Study Scope |
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Keywords: |
Engineering, Engineering, Fault Diagnosis, Domain Adaptation |
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Abstract: |
The dataset contains sensor readings of bearing machines under 4 different operating conditions, with each having 3 different classes, i.e., healthy, inner-bearing damage, and outer-bearing damage. Each operating condition refers to different operating parameters, including rotational speed, load torque, and radial force [. In our experiments, each operating condition is considered as one domain. Eventually, we can perform 12 cross-condition scenarios for domain adaptation. To construct the data samples for each domain, we adopted a sliding window to segment the data into small segments. We set the window size of 5120 and shifting size of 4096 |
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Kind of Data: |
Time series |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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test_a.pt |
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train_a.pt |
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train_d.pt |
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val_a.pt |
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val_c.pt |
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val_d.pt |
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application/octet-stream |