2026-09-18T19:49:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1448012022-08-30T03:06:26Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Leeb, Steven B.
Donnal, John S.
Green, Daisy Hikari
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T16:12:36Z
2022-08-29T16:12:36Z
2022-05
2022-06-21T19:15:36.931Z
https://hdl.handle.net/1721.1/144801
0000-0002-9309-0097
Electromechanical systems provide the world’s backbone for generating and using energy. Electromechanical systems can also experience an innumerable set of failures, causing induced wear and wasted energy, or eventually a complete failure of a critical piece of equipment or system. Degradation or other faults are often associated with subtle but observable changes in electrical consumption. A nonintrusive load monitor (NILM) is a convenient tool for electrical monitoring, in which all loads connected downstream of an electrical panel are monitored with a single set of current and voltage sensors. If collated in a useful way, nonintrusive electrical data can make diagnostic information more easily attainable and improve the efficient operation of critical machines.
Ensuring correct nonintrusive identification of load operation is a challenge in varying operating conditions and fault scenarios. Most nonintrusive load monitoring research assumes that data is static over time. Also, ground truth labels are a scarce resource in industrial scenarios. Thus, a pattern classifier must train on a limited dataset not representative of long-term operation. This thesis employs an understanding of the physics and time-dependency behind changing load behavior to inform pattern classification. New statistical feature extraction techniques are presented for loads with time-varying operation. Results are demonstrated with laboratory experiments and case-studies from NILM installations onboard various marine microgrids.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Electrical Monitoring of Electromechanical Systems
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<Abstract>Electromechanical systems provide the world’s backbone for generating and using energy. Electromechanical systems can also experience an innumerable set of failures, causing induced wear and wasted energy, or eventually a complete failure of a critical piece of equipment or system. Degradation or other faults are often associated with subtle but observable changes in electrical consumption. A nonintrusive load monitor (NILM) is a convenient tool for electrical monitoring, in which all loads connected downstream of an electrical panel are monitored with a single set of current and voltage sensors. If collated in a useful way, nonintrusive electrical data can make diagnostic information more easily attainable and improve the efficient operation of critical machines.
Ensuring correct nonintrusive identification of load operation is a challenge in varying operating conditions and fault scenarios. Most nonintrusive load monitoring research assumes that data is static over time. Also, ground truth labels are a scarce resource in industrial scenarios. Thus, a pattern classifier must train on a limited dataset not representative of long-term operation. This thesis employs an understanding of the physics and time-dependency behind changing load behavior to inform pattern classification. New statistical feature extraction techniques are presented for loads with time-varying operation. Results are demonstrated with laboratory experiments and case-studies from NILM installations onboard various marine microgrids.</Abstract>
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