Machinery diagnostics and characterization through electrical sensing

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Cotta, William Joseph

Advisor dc:contributor.advisor

• Steven B. Leeb, John Donnal and Peter Lindahl.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100144

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100144