2026-09-19T08:32:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1306782026-06-06T00:55:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Steven B. Leeb and Daisy H. Green.
Agustin, Rebecca A.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-05-24T19:40:09Z
2021-05-24T19:40:09Z
2021
2021
https://hdl.handle.net/1721.1/130678
1251770855
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 85-88).
Power monitoring solutions have the potential to collect large amounts of data from the operation of electromechanical loads, such as measurements of power, torque, vibration, and acoustic signals. These measurements can act as unique identifiers for the early identification of degrading system performance, providing a rich feature space for fault detection and diagnostics (FDD). However, mainstream machine learning methods may overlook potential features with key physical context in the development of soft faults due to a lack of faulty load data in publicly available datasets. Therefore, a physically informed feature space must be selected and evaluated specifically for FDD applications in which load behaviors evolve over time. This thesis presents both a method for evaluating a potential load disaggregation feature space and a framework for load classification based on adaptive load benchmarks and health tracking.
by Rebecca A. Agustin.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
88 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A load identification and diagnostic framework for aggregate power monitoring
Thesis
application/pdf
2021-05-24T19:40:09Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Power monitoring solutions have the potential to collect large amounts of data from the operation of electromechanical loads, such as measurements of power, torque, vibration, and acoustic signals. These measurements can act as unique identifiers for the early identification of degrading system performance, providing a rich feature space for fault detection and diagnostics (FDD). However, mainstream machine learning methods may overlook potential features with key physical context in the development of soft faults due to a lack of faulty load data in publicly available datasets. Therefore, a physically informed feature space must be selected and evaluated specifically for FDD applications in which load behaviors evolve over time. This thesis presents both a method for evaluating a potential load disaggregation feature space and a framework for load classification based on adaptive load benchmarks and health tracking.</Abstract>
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