A load identification and diagnostic framework for aggregate power monitoring

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Agustin, Rebecca A.

Advisor dc:contributor.advisor

• Steven B. Leeb and Daisy H. Green.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130678

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130678