Energy Monitor Can Find Electrical Failures Before they Happen

A new system devised by researchers at MIT can monitor the behavior of all electric devices within a building, ship, or factory, determining which ones are in use at any given time and whether any are showing signs of an imminent failure. When tested on a Coast Guard cutter, the system pinpointed a motor with burnt-out wiring that could have led to a serious onboard fire.

Photo shows the area on the Coast Guard cutter Spencer’s diesel engine where the MIT-developed “NILM Dashboard” detected damage that could have caused a fire. The damage was hidden under the brown cap at center. (Image courtesy of the researchers)

The new sensor, whose readings can be monitored on an easy-to-use graphic display called a NILM (non-intrusive load monitoring) dashboard, is described in the March issue of IEEE Transactions on Industrial Informatics, in a paper by MIT professor of electrical engineering Steven Leeb, recent graduate Andre Aboulian MS ’18, and seven others at MIT, the U.S. Coast Guard, and the U.S. Naval Academy. A second paper will appear in the April issue of Marine Technology, the publication of the Society of Naval Architects and Marine Engineers.

The system uses a sensor that simply is attached to the outside of an electrical wire at a single point, without requiring any cutting or splicing of wires. From that single point, it can sense the flow of current in the adjacent wire, and detect the distinctive “signatures” of each motor, pump, or piece of equipment in the circuit by analyzing tiny, unique fluctuations in the voltage and current whenever a device switches on or off. The system can also be used to monitor energy usage, to identify possible efficiency improvements and determine when and where devices are in use or sitting idle.

The technology is especially well-suited for relatively small, contained electrical systems such as those serving a small ship, building, or factory with a limited number of devices to monitor. In a series of tests on a Coast Guard cutter based in Boston, the system provided a dramatic demonstration last year.

About 20 different motors and devices were being tracked by a single dashboard, connected to two different sensors, on the cutter USCGC Spencer. The sensors, which in this case had a hard-wired connection, showed that an anomalous amount of power was being drawn by a component of the ship’s main diesel engines called a jacket water heater. At that point, Leeb says, crewmembers were skeptical about the reading but went to check it anyway. The heaters are hidden under protective metal covers, but as soon as the cover was removed from the suspect device, smoke came pouring out, and severe corrosion and broken insulation were clearly revealed.

“The ship is complicated,” Leeb says. “It’s magnificently run and maintained, but nobody is going to be able to spot everything.”

Lt. Col. Nicholas Galanti, engineer officer on the cutter, says “the advance warning from NILM enabled Spencer to procure and replace these heaters during our in-port maintenance period, and deploy with a fully mission-capable jacket water system. Furthermore, NILM detected a serious shock hazard and may have prevented a class Charlie [electrical] fire in our engine room.”

The system is designed to be easy to use with little training. The computer dashboard features dials for each device being monitored, with needles that will stay in the green zone when things are normal, but swing into the yellow or red zone when a problem is spotted.

Detecting anomalies before they become serious hazards is the dashboard’s primary task, but Leeb points out that it can also perform other useful functions. By constantly monitoring which devices are being used at what times, it could enable energy audits to find devices that were turned on unnecessarily when nobody was using them, or spot less-efficient motors that are drawing more current than their similar counterparts. It could also help ensure that proper maintenance and inspection procedures are being followed, by showing whether or not a device has been activated as scheduled for a given test.

“It’s a three-legged stool,” Leeb says. The system allows for “energy scorekeeping, activity tracking, and condition-based monitoring.” But it’s that last capability that could be crucial, “especially for people with mission-critical systems,” he says. In addition to the Coast Guard and the Navy, he says, that includes companies such as oil producers or chemical manufacturers, who need to monitor factories and field sites that include flammable and hazardous materials and thus require wide safety margins in their operation.

One important characteristic of the system that is attractive for both military and industrial applications, Leeb says, is that all of its computation and analysis can be done locally, within the system itself, and does not require an internet connection at all, so the system can be physically and electronically isolated and thus highly resistant to any outside tampering or data theft.

Although for testing purposes the team has installed both hard-wired and noncontact versions of the monitoring system — both types were installed in different parts of the Coast Guard cutter — the tests have shown that the noncontact version could likely produce sufficient information, making the installation process much simpler. While the anomaly they found on that cutter came from the wired version, Leeb says, “if the noncontact version was installed” in that part of the ship, “we would see almost the same thing.”

The research team also included graduate students Daisy Green, Jennifer Switzer, Thomas Kane, and Peer Lindahl at MIT; Gregory Bredariol of the U.S. Coast Guard; and John Donnal of the U.S. Naval Academy in Annapolis, Maryland. The research was funded by the U.S. Navy’s Office of Naval Research NEPTUNE project, through the MIT Energy Initiative.

Source: https://www.qedenv.com/

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    QED Environmental Systems, Inc.. (2019, March 26). Energy Monitor Can Find Electrical Failures Before they Happen. AZoSensors. Retrieved on February 27, 2024 from https://www.azosensors.com/news.aspx?newsID=12884.

  • MLA

    QED Environmental Systems, Inc.. "Energy Monitor Can Find Electrical Failures Before they Happen". AZoSensors. 27 February 2024. <https://www.azosensors.com/news.aspx?newsID=12884>.

  • Chicago

    QED Environmental Systems, Inc.. "Energy Monitor Can Find Electrical Failures Before they Happen". AZoSensors. https://www.azosensors.com/news.aspx?newsID=12884. (accessed February 27, 2024).

  • Harvard

    QED Environmental Systems, Inc.. 2019. Energy Monitor Can Find Electrical Failures Before they Happen. AZoSensors, viewed 27 February 2024, https://www.azosensors.com/news.aspx?newsID=12884.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit
Azthena logo

AZoM.com powered by Azthena AI

Your AI Assistant finding answers from trusted AZoM content

Azthena logo with the word Azthena

Your AI Powered Scientific Assistant

Hi, I'm Azthena, you can trust me to find commercial scientific answers from AZoNetwork.com.

A few things you need to know before we start. Please read and accept to continue.

  • Use of “Azthena” is subject to the terms and conditions of use as set out by OpenAI.
  • Content provided on any AZoNetwork sites are subject to the site Terms & Conditions and Privacy Policy.
  • Large Language Models can make mistakes. Consider checking important information.

Great. Ask your question.

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.