{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/144801"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/144801","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Electrical Monitoring of Electromechanical Systems","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_html":"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_has_math":false,"creators":["Green, Daisy Hikari"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Leeb, Steven B.","Donnal, John S."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:22:18Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/144801","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Leeb, Steven B.","Donnal, John S."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Green, Daisy Hikari"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-08-29T16:12:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-08-29T16:12:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/144801"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Electrical Monitoring of Electromechanical Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Leeb, Steven B.","Donnal, John S."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Green, Daisy Hikari"],"dc:date.accessioned":["2022-08-29T16:12:36Z"],"dc:date.available":["2022-08-29T16:12:36Z"],"dc:date.issued":["2022-05"],"dc:description.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."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/144801"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Electrical Monitoring of Electromechanical Systems"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:22:18Z"}