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Part 1: Document Description
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Citation |
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Title: |
Machine Fault Diagnosis |
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Identification Number: |
doi:10.21979/N9/PU85XN |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2022-12-30 |
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Version: |
1 |
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Bibliographic Citation: |
Ragab, Mohamed, 2022, "Machine Fault Diagnosis", https://doi.org/10.21979/N9/PU85XN, DR-NTU (Data), V1 |
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Citation |
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Title: |
Machine Fault Diagnosis |
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Identification Number: |
doi:10.21979/N9/PU85XN |
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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: |
2022-12-30 |
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Holdings Information: |
https://doi.org/10.21979/N9/PU85XN |
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Study Scope |
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Keywords: |
Computer and Information Science, Engineering, Computer and Information Science, Engineering, Fault Diagnosis, Deep Learning, Domain Adaptation |
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Abstract: |
The dataset consists of sensor readings from bearing machines under four different operating conditions. Each of these conditions has three classes: healthy, inner-bearing damage, and outer-bearing damage. The operating conditions refer to different values for rotational speed, load torque, and radial force. In our experiments, each operating condition is treated as a separate domain. This allows us to perform 12 cross-condition scenarios for domain adaptation. To create the data samples for each domain, we used a sliding window technique to divide the data into smaller segments. The window size was set to 5120, with a shift 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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Related Studies |
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The code is available at <a href="https://github.com/emadeldeen24/AdaTime">https://github.com/emadeldeen24/AdaTime</a> |
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Related Publications |
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Citation |
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Identification Number: |
10.48550/arXiv.2203.08321 |
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Bibliographic Citation: |
Ragab, M., Eldele, E., Tan, W. L., Foo, C. S., Chen, Z., Wu, M., ... & Li, X. (2022). ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data. arXiv preprint arXiv:2203.08321. |
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