Back to news

March 23, 2020 | International, Land, C4ISR

Raytheon AI: Fix That Part Before It Breaks

A modern mechanized military lives or dies by maintenance. But what if a computer could warn you when your weapons and vehicles were about to break, so you could fix them before they ever let you down?

By

WASHINGTON: Raytheon is working with the military on multiple pilot projects for AI-driven predictive maintenance.

What's that? Traditionally, military mechanics spend a huge amount of time on what's called preventive maintenance: They carry truckloads of spare parts to war, they consult historical tables of roughly how often certain parts wear out or break down, and they preemptively crack open the access hatches to check those parts on a regular basis. The idea behind predictive maintenance is to feed all that historical data into a machine learning algorithm so it can tell maintainers, vehicle by vehicle and part by part, when something is likely to fail.

It's a tremendous technical challenge that requires scanning in years of old handwritten maintenance forms, downloading digital records, and then constantly updating the database. Ideally, you want up-to-the-minute reports on things like engine temperature and suspension stress from diagnostic sensors installed in frontline vehicles.

You need to account not only for what kind of equipment you're operating, but how hard it's running for a particular mission and even where in the world it's operating, because environmental conditions like heat, moisture, dust, and sand make a huge difference to wear and tear. And you can't just push out a single software solution and call it done. You have to constantly update your data so the algorithm can continue to learn, evolve, and adapt to different situations.

But, Raytheon's Kevin Frazier and Butch Kievenaar told me, artificial intelligence and machine learning have advanced dramatically over just the last five years. Now Raytheon – a long-established defense contractor – is partnered with a flock of niche innovators to make it happen.

Currently, they told me, Raytheon is already conducting or about to launch several multi-month pilot projects, seeking to prove the technology's value to the military:

  • For the Army, they're working with a commercial partner on the M2 Bradley Infantry Fighting Vehicle, the mainstay armored troop transport of the heavy combat brigades, and the hulking M88 Hercules, a tracked “armored recovery vehicle” designed to tow broken-down battle tanks back for repair, if necessary under enemy fire.
  • For the V-22 Joint Program Office – which supports the Osprey tiltrotor for the Marines, Air Force Special Operations Command, and now the Navy – they're working on the V-22's collision-avoidance radar, a Raytheon product.
  • And across their customer base, they're looking at ways to do predictive maintenance on the many complex components Raytheon provides for a host of programs.

How does this work? Let's hear from Kevin and Butch in their own words (edited for clarity and brevity from a highly technical 50-minute interview):

Q: What kinds of problems can this technology help the military solve?

Kevin: Right now, maintenance is conducted either on a scheduled timeline or when something breaks. What we are trying to do is replace that one piece because you know it's about to wear out and prevent it from breaking.

Butch: One of the biggest things is you've got to understand what mission you're trying to achieve. If I'm trying to answer platform readiness questions, then I have to have certain data that's related to that topic. If I am trying to do supply chain analysis, I'm asking questions about where are critical parts and what size stockages we have to have to reduce turnaround time. So I'm answering a different question, and I'm looking at a different data set.

So the key to setting all this up is what you do on the front end with your data to give the data scientists so that we can refine the algorithm appropriately.

Q: AI/ML requires a lot of data. Is that data really available for all these different military systems?

Kevin: It is. It's in different states. Some vehicles have sensors on them. Some do self-diagnostics. Some of the older equipment, especially the support equipment, doesn't have any sensors on them — but they all have files. They all are in the maintenance system, so the data exists.

Data doesn't have to purely digital. It does have to be digitized at some point, but it doesn't necessarily have to start being digital. It could be maintenance logs that are hand-written, or the operator of a particular vehicle does a walk around and does an inspection report, writes that up — that's something that you actually can scan and input.

Now we can add so many different types of data that your whole data environment becomes much richer. It helps you get to that algorithm — and then to continue to take in that data and refine that model. You're still recording that data and getting data from both handwritten and digital sources to update your model and tune it, so that you're just that much more accurate.

Butch: What we're talking about is discrete algorithms solving for discrete problem sets. You look at the environment, and what the algorithm does is it learns.

You keep ingesting data. You can get it a bunch of different ways so your analytical tool continues to learn, continues to refine. I can do a physical download from the vehicle, or scan maintenance records, or get it all fed off of a downloader that automatically feeds to the cloud. It can be as fast as we can automate the process of that piece of equipment feeding information back.

For the Army and the Air Force especially, there is sufficient data over the last 15 that pertains to the impacts of combat. And we have it for different environments that you can then use to help train and refine the algorithms that you're using as it learns.

Kevin: You have to understand the impacts the environment has on how the vehicle is functioning and what type of a mission you're doing, because that will cause different things to wear out sooner or break sooner.

That's what the AI piece does. The small companies that we partner with, who are very good at these algorithms, already do this to some extent in the commercial world. We're trying to bring that to the military.

Butch: The really smart data scientists are in a lot of the smaller niche companies that are doing this. We combine their tools with our ability to scale and wrap around the customer's needs.

These are not huge challenges that we're talking about trying to solve. It is inside the current technological capability that exists. We have currently several pilot programs right now to demonstrate the use cases, that this capability that actually works.

https://breakingdefense.com/2020/03/raytheon-ai-fix-that-part-before-it-breaks

On the same subject

  • Funding for naval drones in the NDAA will encourage innovation

    July 27, 2020 | International, Aerospace, Naval

    Funding for naval drones in the NDAA will encourage innovation

    By: Brian Wynne Unmanned maritime systems are increasingly allowing military and commercial users alike to go farther and deeper than ever before. Initially proven by the military for their dependability and reliability, they are now also disrupting the commercial sector and enabling applications from mapping to surveillance to port security. In recognition of the many benefits UMS stand to offer, the president's budget for fiscal 2021 requested strong support for the U.S. Navy's unmanned programs. Now, as Congress considers the National Defense Authorization Act for FY21, it should fully fund UMS research and development efforts to allow innovation to flourish and for military and commercial operators alike to reap the benefits. As president and CEO of the Association for Unmanned Vehicle Systems International, I have witnessed the growth in UMS innovation firsthand. Our membership includes organizations from across the defense industrial base that support the growing integration of unmanned and autonomous systems in the force protecting the United States. Their investments have led to substantive achievements in the development of autonomy, reliability, propulsion and integration of advanced payloads and sensors. Fielding UMS will ensure continued U.S. naval dominance and support the industrial base. Unfortunately, Congress is currently considering disrupting funding to the research and development of this vital technology. Both the House and Senate versions of the NDAA drastically cut R&D funding for medium and large unmanned surface vessels, with the Senate eliminating all requested funding for the program entirely. The severe reduction in funding considered in the FY21 NDAA would eliminate jobs, drive many small companies out of business, and cause larger companies to shift their R&D investments to more stable opportunities. AUVSI is also taking issue with Congress' misunderstanding of UMS operations, focusing on the reliability of individual components rather than that of the system as a whole, ignoring the operational context in which the UMS will be used. Unmanned systems have well-documented reliability in the commercial sector performing in a range of demanding and complex environments, including deep-water exploration. If Congress attempts to apply unique reliability requirements to UMS use by the U.S. Navy, it will only serve to drive up cost, decrease competition and significantly delay fielding of the systems to the war fighters that need them. While Congress has previously demonstrated its support for the growth and integration of unmanned systems in the future Navy fleet architecture, its reliability concerns and proposed funding cuts in this instance are misplaced. Industry has determined that the wholeness of autonomy is critical to mission duration and success, and the emphasis on testing reliability should be on that wholeness rather than focusing on individual components. What's more, the Navy's R&D effort is already working to field systems that can prove reliability in a realistic operational context. The utilization of unmanned technology is inevitable and timely, but appropriate levels of R&D funding are needed to field this critical capability. Industry has invested significant resources to support the Navy's UMS programs thus far and will continue to do so if these programs are adequately funded by Congress. Conversely, proposed funding cuts will drive industry to move its investments away from UMS to other markets, drive small, developing businesses out of the unmanned maritime business, and cost jobs throughout the developing unmanned industrial base. Congress should therefore adopt the funding levels set out in the president's FY21 budget request without any cuts to ensure that innovation will flourish, R&D can continue unabated and our nation's Navy can take full advantage of the potential that UMS stand to offer. https://www.defensenews.com/opinion/commentary/2020/07/24/funding-for-naval-drones-in-the-ndaa-will-encourage-innovation/

  • DARPA Funding Brings Machine Learning to BAE Systems’ Signals Intelligence Capabilities

    July 8, 2019 | International, Aerospace, Other Defence

    DARPA Funding Brings Machine Learning to BAE Systems’ Signals Intelligence Capabilities

    HUDSON, N.H.--(BUSINESS WIRE)--BAE Systems has been awarded funding from the Defense Advanced Research Projects Agency (DARPA) to integrate machine-learning (ML) technology into platforms that decipher radio frequency signals. Its Controllable Hardware Integration for Machine-learning Enabled Real-time Adaptivity (CHIMERA) solution provides a reconfigurable hardware platform for ML algorithm developers to make sense of radio frequency (RF) signals in increasingly crowded electromagnetic spectrum environments. The up to $4.7 million contract, dependent on successful completion of milestones, includes hardware delivery along with integration and demonstration support. CHIMERA's hardware platform will enable algorithm developers to decipher the ever-growing number of RF signals, providing commercial or military users with greater automated situational awareness of their operating environment. This contract is adjacent to the previously announced award for the development of data-driven ML algorithms under the same DARPA program (Radio Frequency Machine Learning Systems, or RFMLS). RFMLS requires a robust, adaptable hardware solution with a multitude of control surfaces to enable improved discrimination of signals in the evolving dense spectrum environments of the future. “CHIMERA brings the flexibility of a software solution to hardware,” said Dave Logan, vice president and general manager of Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) Systems at BAE Systems. “Machine-learning is on the verge of revolutionizing signals intelligence technology, just as it has in other industries.” In an evolving threat environment, CHIMERA will enable ML software development to adapt the hardware's RF configuration in real time to optimize mission performance. This capability has never before been available in a hardware solution. The system provides multiple control surfaces for the user, enabling on-the-fly performance trade-offs that can maximize its sensitivity, selectivity, and scalability depending on mission need. The system's open architecture interfaces allow for third party algorithm development, making the system future-proof and easily upgradable upon deployment. Other RF functions, including communications, radar, and electronic warfare, also can benefit from this agile hardware platform, which has a reconfigurable array, front-end, full transceiver and digital pre-processing stage. Work on these phases of the program will take place at BAE Systems' sites in Hudson and Merrimack, New Hampshire, and Dallas, Texas. https://www.businesswire.com/news/home/20190708005199/en

  • Joint Statement from FBI and CISA on the People's Republic of China (PRC) Targeting of Commercial Telecommunications Infrastructure | CISA

    November 13, 2024 | International, C4ISR, Security

    Joint Statement from FBI and CISA on the People's Republic of China (PRC) Targeting of Commercial Telecommunications Infrastructure | CISA

All news