Shipboard Fault Detection Methods for Condition-based Maintenance
Vibration analysis can measure and track machine health. Computational advances in signal processing that leverage spectral coherence to identify subtle shifts in cyclostationary behavior provide new opportunities in vibration-based monitoring. The acquisition of vibration measurements must overcome...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144542 https://orcid.org/0000-0001-5704-1477 |
| _version_ | 1826216739957899264 |
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| author | Quinn, Devin Wayne |
| author2 | Leeb, Steven B. |
| author_facet | Leeb, Steven B. Quinn, Devin Wayne |
| author_sort | Quinn, Devin Wayne |
| collection | MIT |
| description | Vibration analysis can measure and track machine health. Computational advances in signal processing that leverage spectral coherence to identify subtle shifts in cyclostationary behavior provide new opportunities in vibration-based monitoring. The acquisition of vibration measurements must overcome significant practical challenges for successful vibration analysis. This work demonstrates vibration analysis for shipboard fault detection. Custom instrumentation and measurement techniques are applied to compressors, pumps, fans, and other shipboard electric machines. |
| first_indexed | 2024-09-23T16:52:21Z |
| format | Thesis |
| id | mit-1721.1/144542 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:52:21Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1445422022-08-30T03:02:14Z Shipboard Fault Detection Methods for Condition-based Maintenance Quinn, Devin Wayne Leeb, Steven B. Krause, Thomas C. Massachusetts Institute of Technology. Department of Mechanical Engineering Vibration analysis can measure and track machine health. Computational advances in signal processing that leverage spectral coherence to identify subtle shifts in cyclostationary behavior provide new opportunities in vibration-based monitoring. The acquisition of vibration measurements must overcome significant practical challenges for successful vibration analysis. This work demonstrates vibration analysis for shipboard fault detection. Custom instrumentation and measurement techniques are applied to compressors, pumps, fans, and other shipboard electric machines. S.M. 2022-08-29T15:54:40Z 2022-08-29T15:54:40Z 2022-05 2022-06-23T14:10:28.756Z Thesis https://hdl.handle.net/1721.1/144542 https://orcid.org/0000-0001-5704-1477 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Quinn, Devin Wayne Shipboard Fault Detection Methods for Condition-based Maintenance |
| title | Shipboard Fault Detection Methods for Condition-based Maintenance |
| title_full | Shipboard Fault Detection Methods for Condition-based Maintenance |
| title_fullStr | Shipboard Fault Detection Methods for Condition-based Maintenance |
| title_full_unstemmed | Shipboard Fault Detection Methods for Condition-based Maintenance |
| title_short | Shipboard Fault Detection Methods for Condition-based Maintenance |
| title_sort | shipboard fault detection methods for condition based maintenance |
| url | https://hdl.handle.net/1721.1/144542 https://orcid.org/0000-0001-5704-1477 |
| work_keys_str_mv | AT quinndevinwayne shipboardfaultdetectionmethodsforconditionbasedmaintenance |