Overview of Publicly Available Degradation Data Sets for Tasks within Prognostics and Health Management
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915776220364800 |
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| author | Mauthe, Fabian Braun, Christopher Raible, Julian Zeiler, Peter Huber, Marco F. |
| author_facet | Mauthe, Fabian Braun, Christopher Raible, Julian Zeiler, Peter Huber, Marco F. |
| contents | Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of information, offering invaluable insights into the underlying degradation processes, failure modes, and performance trends of engineering systems. This paper provides an overview of publicly available degradation data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13694 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overview of Publicly Available Degradation Data Sets for Tasks within Prognostics and Health Management Mauthe, Fabian Braun, Christopher Raible, Julian Zeiler, Peter Huber, Marco F. Databases Signal Processing Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of information, offering invaluable insights into the underlying degradation processes, failure modes, and performance trends of engineering systems. This paper provides an overview of publicly available degradation data sets. |
| title | Overview of Publicly Available Degradation Data Sets for Tasks within Prognostics and Health Management |
| topic | Databases Signal Processing |
| url | https://arxiv.org/abs/2403.13694 |