Uncertainty in Prognostics and Systems Health Management
This paper presents an overview of various aspects of uncertainty quantification and management in prognostics and systems health management. Prognostics deals with predicting possible future failures in different types of engineering systems. It is almost practically impossible to precisely predict...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Prognostics and Health Management Society
2015-12-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2319 |
| _version_ | 1818576822326001664 |
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| author | Shankar Sankararaman Kai Goebel |
| author_facet | Shankar Sankararaman Kai Goebel |
| author_sort | Shankar Sankararaman |
| collection | DOAJ |
| description | This paper presents an overview of various aspects of uncertainty quantification and management in prognostics and systems health management. Prognostics deals with predicting possible future failures in different types of engineering systems. It is almost practically impossible to precisely predict future events; therefore, it is necessary to account for the different sources of uncertainty that affect prognostics, and develop a systematic framework for uncertainty quantification and management in this context. Researchers have developed computational methods for prognostics, both in the context of testing-based health management and condition-based health management. This paper explains that the interpretation
of uncertainty for these two different types of situations is completely different. While both the frequentist (based on the presence of true variability) and Bayesian (based on subjective assessment) approaches are applicable in the context of testing-based health management, only the Bayesian approach is applicable in the context of condition-based health management. This paper illustrates that the computation of the remaining useful life is more meaningful in the context of condition-based monitoring and needs to be approached
as an uncertainty propagation problem. Further, uncertainty management issues are discussed and possible solutions are explored. Numerical examples are presented to illustrate the various concepts discussed in the paper. |
| first_indexed | 2024-12-16T06:20:07Z |
| format | Article |
| id | doaj.art-7321ae94750f410cb7311ba65b6e876f |
| institution | Directory Open Access Journal |
| issn | 2153-2648 2153-2648 |
| language | English |
| last_indexed | 2024-12-16T06:20:07Z |
| publishDate | 2015-12-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-7321ae94750f410cb7311ba65b6e876f2022-12-21T22:41:09ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482015-12-0164doi:10.36001/ijphm.2015.v6i4.2319Uncertainty in Prognostics and Systems Health ManagementShankar Sankararaman0Kai Goebel1SGT Inc., NASA Ames Research Center, Moffett Field, CA 94035NASA Ames Research Center, Moffett Field, CA 94035This paper presents an overview of various aspects of uncertainty quantification and management in prognostics and systems health management. Prognostics deals with predicting possible future failures in different types of engineering systems. It is almost practically impossible to precisely predict future events; therefore, it is necessary to account for the different sources of uncertainty that affect prognostics, and develop a systematic framework for uncertainty quantification and management in this context. Researchers have developed computational methods for prognostics, both in the context of testing-based health management and condition-based health management. This paper explains that the interpretation of uncertainty for these two different types of situations is completely different. While both the frequentist (based on the presence of true variability) and Bayesian (based on subjective assessment) approaches are applicable in the context of testing-based health management, only the Bayesian approach is applicable in the context of condition-based health management. This paper illustrates that the computation of the remaining useful life is more meaningful in the context of condition-based monitoring and needs to be approached as an uncertainty propagation problem. Further, uncertainty management issues are discussed and possible solutions are explored. Numerical examples are presented to illustrate the various concepts discussed in the paper.https://papers.phmsociety.org/index.php/ijphm/article/view/2319prognosticssensitivity analysisuncertaintyremaining useful life prediction |
| spellingShingle | Shankar Sankararaman Kai Goebel Uncertainty in Prognostics and Systems Health Management International Journal of Prognostics and Health Management prognostics sensitivity analysis uncertainty remaining useful life prediction |
| title | Uncertainty in Prognostics and Systems Health Management |
| title_full | Uncertainty in Prognostics and Systems Health Management |
| title_fullStr | Uncertainty in Prognostics and Systems Health Management |
| title_full_unstemmed | Uncertainty in Prognostics and Systems Health Management |
| title_short | Uncertainty in Prognostics and Systems Health Management |
| title_sort | uncertainty in prognostics and systems health management |
| topic | prognostics sensitivity analysis uncertainty remaining useful life prediction |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/2319 |
| work_keys_str_mv | AT shankarsankararaman uncertaintyinprognosticsandsystemshealthmanagement AT kaigoebel uncertaintyinprognosticsandsystemshealthmanagement |