Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets
Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algorithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Research Center. Among those datasets are five run-to-failure CMAPSS da...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2014-06-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2236 |
| _version_ | 1819126225342300160 |
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| author | Emmanuel Ramasso Abhinav Saxena |
| author_facet | Emmanuel Ramasso Abhinav Saxena |
| author_sort | Emmanuel Ramasso |
| collection | DOAJ |
| description | Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algorithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Research Center. Among those datasets are five run-to-failure CMAPSS datasets that have been popular due to various characteristics applicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognostics applications. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than seventy publications have used the C-MAPSS datasets for developing datadriven prognostic algorithms. However, in the absence of performance benchmarking results and due to common misunderstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating characteristics in these datasets, this paper also provides performance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes various prognostic modeling efforts that used C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches. |
| first_indexed | 2024-12-22T07:52:39Z |
| format | Article |
| id | doaj.art-a1733d6b90614b18aa375234ff837f24 |
| institution | Directory Open Access Journal |
| issn | 2153-2648 2153-2648 |
| language | English |
| last_indexed | 2024-12-22T07:52:39Z |
| publishDate | 2014-06-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-a1733d6b90614b18aa375234ff837f242022-12-21T18:33:27ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482014-06-0152doi:10.36001/ijphm.2014.v5i2.2236Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS DatasetsEmmanuel Ramasso0Abhinav Saxena1FEMTO-ST Institute, Dep. AS2M/DMA, UMR CNRS 6174 - UFC / ENSMM / UTBM, 25000 Besanc¸on, FranceSGT Inc., NASA Ames Research Center, Intelligent Systems Division, Moffett Field, CA, 94035-1000, USASix years and more than seventy publications later this paper looks back and analyzes the development of prognostic algorithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Research Center. Among those datasets are five run-to-failure CMAPSS datasets that have been popular due to various characteristics applicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognostics applications. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than seventy publications have used the C-MAPSS datasets for developing datadriven prognostic algorithms. However, in the absence of performance benchmarking results and due to common misunderstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating characteristics in these datasets, this paper also provides performance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes various prognostic modeling efforts that used C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.https://papers.phmsociety.org/index.php/ijphm/article/view/2236prognosticsreviewbenchmarkingc-mapss datasets |
| spellingShingle | Emmanuel Ramasso Abhinav Saxena Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets International Journal of Prognostics and Health Management prognostics review benchmarking c-mapss datasets |
| title | Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets |
| title_full | Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets |
| title_fullStr | Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets |
| title_full_unstemmed | Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets |
| title_short | Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets |
| title_sort | performance benchmarking and analysis of prognostic methods for cmapss datasets |
| topic | prognostics review benchmarking c-mapss datasets |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/2236 |
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