21 août 2023 | International, Aérospatial

US State Dept OKs possible sale of Apache helicopters to Poland for $12 bln -Pentagon | Reuters

The U.S. State Department has approved the potential sale of AH-64E Apache helicopters and related equipment to Poland in a deal valued at up to $12 billion, the Pentagon said on Monday.


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  • Raytheon AI: Fix That Part Before It Breaks

    23 mars 2020 | International, Terrestre, 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 SYDNEY J. FREEDBERG JR. 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

  • US Army to free up another $10 billion for priorities

    3 juin 2019 | International, Terrestre

    US Army to free up another $10 billion for priorities

    By: Jen Judson WASHINGTON — The U.S. Army is freeing up another $10 billion to apply to its top priorities in its next five-year budget plan, according to the service's undersecretary. “We are about to slap the table on the [program objective memorandum] here by no later than the middle of June,” Ryan McCarthy told a group of reporters during a May 29 media roundtable in his office. As part of a rigorous review of programs and spending, the Army set out to find $10 billion within the budget that could be reallocated toward priorities in its fiscal 2021-2025 program objective memorandum. The money shook out through another round of what the Army informally calls “night court,” a review process that freed up $30 billion in the last budget cycle to get ambitious modernization programs off the ground. The night court process was inspired by similar reviews conducted under Robert Gates when he was defense secretary. Rather than make $182 billion worth of decisions in a few hours, the process is meant to establish a deliberate route to applying funds against priorities, McCarthy said. For example, if a program didn't contribute to a more lethal battlefield or to one of the Army's six modernization priorities, it was canceled or downsized. The Army set up a new four-star command — Army Futures Command — last year to tackle the service's top six modernization priorities: long-range precision fires, the next-generation combat vehicle, future vertical lift, the network, air and missile defense, and soldier lethality. The review was conducted with the Army chief, vice chief, secretary and undersecretary at the head of the table last summer. But this year, to establish a more sustainable model, leadership fell to the major four-star commands and civilian heads in charge of major offices like acquisition and manpower. “Every dollar counts in this environment,” McCarthy said. “And so what we've done is we've realized that it's not a sustainable model to have the entire Army leadership hunkered down every summer, but should delegate to the appropriate echelon of authority.” Only the most difficult decisions will be brought to the top four Army leaders, he added. When it comes to finding another $10 billion across the five-year planning period to apply to priorities, McCarthy said, “we are in very good shape there.” The Army is also working to shift spending so that 50 percent is applied to new programs and 50 percent to legacy systems in the FY24-FY25 time frame. In FY17, the Army was applying 80 percent to legacy programs and 20 percent to bringing on new capabilities. https://www.defensenews.com/land/2019/05/31/army-freeing-up-another-10-billion-for-priorities/

  • Navy Looking for Better Ways to Share Data

    21 juin 2019 | International, Naval

    Navy Looking for Better Ways to Share Data

    By: Ben Werner WASHINGTON, D.C. – The Navy is grappling with how to securely share the vast amounts of data ship designers, operators and sustainers collect, a panel of engineers said Wednesday. Shipyards have the design systems they use to transmit plans from engineers to the shipbuilders. Once delivered, modern ships, submarines and even aircraft generate tremendous amounts of data gauging their performance. The Navy has more data than it knows what to do with, but Rear Adm. Lorin Selby wants to change this. “The problem we have is we don't do a great job of linking those together,” Selby said of the various data points. “That's what I'm driving for trying to link those together.” Selby, the chief engineer and deputy commander for ship design, integration and naval engineer at the Naval Sea Systems Command, was speaking as part of a panel discussing how the Navy and shipbuilding industry can use digital plans at the American Society of Naval Engineering's annual Technology, Systems & Ship symposium. Selby was joined by Rear Adm. Eric Ver Hage, the commander of the Naval Surface Warfare Center and Naval Undersea Warfare Center, and Zac Staples, a retired commander and current chief executive of Austin, Texas,-based maritime analytics firm FATHOM5. Staples' final tour in the Navy was the director of the Center for Cyber Warfare at the Naval Postgraduate School. “Today, we know the liability of many of our systems. We know the ship loadout. We know the type of baseline the ships have. We know the performance of tactical action officers and other key watchstanders when they're in the basic training cycle. We know the proficiency of the strike group when they go to sea,” Ver Hage said. “You have all this data; the problem is, we put missiles on ships, but the combat systems can't unlock all the capability that missile has in some instances,” he said, referring the possibility communications between ships and missiles could improve targeting. When quantum computing is developed, the ability to process this massive amount of data will become much easier, Selby said. Quantum computing is still being researched, with several nations trying to develop a way to tackle large data sets quickly, Selby explained. Within a year or two of mastering quantum computing, he predicts everyone will be able to use quantum computing. For the U.S. to have a decisive quantum computing edge, Selby said requires being ready now. “The key to being the one who can actually lever that technology and really take a huge leap forward in this century is going to be the nation that lays the foundation to be able to lever the capabilities of quantum with a software delivery mechanism,” Selby said. However, as the ability to analyze data speeds up, the importance of protecting this data also grows. “If we're going to build capabilities in the era of great power competition, we have to assume our adversaries are trying to steal them – because they're trying to steal them,” Staples said. “The exact copy Chinese joint strike fighter is a good indication that whatever our shipboard capabilities might go for will be equally targeted.” The current secure method of transferring data classified up to the secret level is over the Secret Internet Protocol Router Network (SIPRNet). However, SIPRNet has limitations, such as the expense of operating the network and creating secure terminals so everyone has access to a SIPRNet terminal to send and receive classified secret information. A cloud-based data vault could prove to be a good solution. Under such a program, access can be restricted, Staples said. Vault monitors will also know which adversaries are denied access to the valuable data being stored. “When you think about encrypting data, there's probably a more efficient way to do that than on SIPRNet,” Staples said. https://news.usni.org/2019/06/20/navy-looking-for-better-ways-to-share-data

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