2026-09-19T02:23:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1001442022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Steven B. Leeb, John Donnal and Peter Lindahl.
Cotta, William Joseph
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2015-12-03T20:56:15Z
2015-12-03T20:56:15Z
2015
2015
http://hdl.handle.net/1721.1/100144
930148280
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.
151 pages
eng
Massachusetts Institute of Technology
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
Mechanical Engineering.
Machinery diagnostics and characterization through electrical sensing
Thesis
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Publication
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