A load identification and diagnostic framework for aggregate power monitoring

• Main Author: Agustin, Rebecca A.
• Other Authors: Steven B. Leeb and Daisy H. Green.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2021
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/130678

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