Shipboard Fault Detection Through Nonintrusive Load Monitoring: A Case Study
Name
shipboard_NILM.pdf
Description
Accepted version
Size
3.91 MB
Format
Adobe PDF
Checksum (MD5)
35f3dc5ce589e0e336112255b0551b9e
Author(s)
Lindahl, Peter A
Green, Daisy H
Bredariol, Gregory
Aboulian, Andre
Donnal, John S
Leeb, Steven B
Date Issued
2018
Journal
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2001-2012 IEEE. As crew sizes aboard maritime vessels shrink in efforts to reduce operational costs, ship operators increasingly rely on advanced monitoring systems to ensure proper operation of shipboard equipment. The nonintrusive load monitor (NILM) is an inexpensive, robust, and easy to install system useful for this task. NILMs measure power data at centralized locations in ship electric grids and disaggregate power draws of individual electric loads. This data contains information related to the health of shipboard equipment. We present a NILM-based framework for performing fault detection and isolation, with a particular emphasis on systems employing closed-loop hysteresis control. Such controllers can mask component faults, eventually leading to damaging system failure. The NILM system uses a neural network for load disaggregation and calculates operational metrics related to machinery health. We demonstrate the framework's effectiveness using data collected from two NILMs installed aboard a U.S. Coast Guard cutter. The NILMs accurately disaggregate loads, and the diagnostic metrics provide easy distinction of several faults in the gray water disposal system. Early detection of such faults prevents costly wear and avoids catastrophic failures.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/JSEN.2018.2869115
