Machinery diagnostics and characterization through electrical sensing
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.
| Main Author: | |
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2015
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| Online Access: | http://hdl.handle.net/1721.1/100144 |
| _version_ | 1811083890420350976 |
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| author | Cotta, William Joseph |
| author2 | Steven B. Leeb, John Donnal and Peter Lindahl. |
| author_facet | Steven B. Leeb, John Donnal and Peter Lindahl. Cotta, William Joseph |
| author_sort | Cotta, William Joseph |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. |
| first_indexed | 2024-09-23T12:41:14Z |
| format | Thesis |
| id | mit-1721.1/100144 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:41:14Z |
| publishDate | 2015 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1001442019-04-10T15:40:08Z Machinery diagnostics and characterization through electrical sensing Cotta, William Joseph Steven B. Leeb, John Donnal and Peter Lindahl. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering. Mechanical Engineering. Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 149-151). Two methods of non intrusive sensing and their applications for machinery condition monitoring, energy score keeping, and human activity are presented here. The first method uses existing research on Non Intrusive Load Monitoring (NILM) to refine transient detection methods using image classification techniques. Additionally building on the NilmDB framework, a new framework, TransientDB, is proposed which collects and stores information about detected transients for use in machine learning algorithms. Finally the military and civilian applications of NILM developed from multiple field tests are presented. The second method presented determines the health of machinery resilient mounts using vibration and voltage sensing, this method was developed using a multiple lab experiments, and it's utility is demonstrated in field testing on US Navy ships. by William Joseph Cotta. S.M. 2015-12-03T20:56:15Z 2015-12-03T20:56:15Z 2015 2015 Thesis http://hdl.handle.net/1721.1/100144 930148280 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 151 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Cotta, William Joseph Machinery diagnostics and characterization through electrical sensing |
| title | Machinery diagnostics and characterization through electrical sensing |
| title_full | Machinery diagnostics and characterization through electrical sensing |
| title_fullStr | Machinery diagnostics and characterization through electrical sensing |
| title_full_unstemmed | Machinery diagnostics and characterization through electrical sensing |
| title_short | Machinery diagnostics and characterization through electrical sensing |
| title_sort | machinery diagnostics and characterization through electrical sensing |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/100144 |
| work_keys_str_mv | AT cottawilliamjoseph machinerydiagnosticsandcharacterizationthroughelectricalsensing |