The Army recently drove tactical trucks with sensors, electronics, and other applications powered by commercially-developed artificial intelligence technology — such as IBM's Watson — as a way to take new steps in more quickly predicting and identifying mechanical failures of great relevance to combat operations.
Described by participants as a "bake-off," an Army-industry assessment incorporated attempts to use AI and real-time data analytics for newer, fast-evolving applications of conditioned-based maintenance technology.
Advanced computer algorithms, enhanced in some instances through machine learning, enable systems, such as Watson, to instantly draw upon vast volumes of historical data as a way to expedite analysis of key mechanical indicators. Real-time analytics, drawing upon documented pools of established data through computer automation, can integrate otherwise disconnected sensors and other onboard vehicle systems.
"We identified some of the challenges in how you harmonize sensor data that is delivered from different solutions. Kevin Aven, partner and co-account lead, Army and Marine Corps, IBM Global Business Services, told Warrior Maven in an interview.
This cargo truck, the M1083A1P2, represents today's current fleet of medium tactical vehicles. (Photo by U.S. Army)
Watson, for example, can take unstructured information from maintenance manuals, reports, safety materials, vehicle history information, and other vehicle technologies and use AI to analyze data and draw informed conclusions of great significance to military operators, Aven explained.
When created, IBM stated that "more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses," according to IBM Systems and Technology.
Faster diagnostics, of course, enable vehicle operators to anticipate when various failures, such as engine or transmission challenges, may happen in advance of a potentially disruptive battlefield event. Alongside an unmistakable operational benefit, faster conditioned-based maintenance activity also greatly streamlines the logistics train, optimizes repairs, and reduces costs for the Army.
Army wheeled tactical vehicles, which include things like the family of medium tactical vehicles and emerging Joint Light Tactical Vehicle, are moving towards using more automation and AI to gather, organize, and analyze sensor data and key technical indicators from onboard systems.
"We identified Army data challenges, delivered new sensors – and used different approaches – invariably bringing on different ways that data can be delivered to the Army," Aven added.
Faster computer processing brings substantial advantages to Army vehicles which increasingly rely upon networked electronics, sensors, and C4ISR systems.
Multiple vendors took part in the industry "bake-off" event, which included participation from the Army Research Laboratory (ARL); the ARL is among a number of Army and DoD entities now accelerating development and integration of AI into a wide range of military technologies.
"We know there is going to be unmanned systems for the future, and we want to look at unmanned systems and working with teams of manned systems. This involves AI-enabled machine learning in high priority areas we know are going to be long term as well as near term applications," Karl Kappra, Chief of the Office of Strategy Mangement for the Army Research Lab, told Warrior Maven in an interview. "We also know we are going to be operating in complex environments, including electromagnetic and cyber areas."
Technical gains in the area of AI and autonomy are arriving at lightning speed, offering faster, more efficient technical functions across a wide range of platforms. Years ago, the Army began experimenting with "leader-follower" algorithms designed to program an unmanned tactical vehicle to follow a manned vehicle, mirroring its movements.
Autonomous or semi-autonomous navigation, quite naturally, brings a range of combat advantages. A truck able to drive itself can, among other things, free up vehicle operators for other high-priority combat tasks.
AI-enabled CBM can function through a variety of methods; sensor information can be gathered, organized, and then subsequently downloaded or wirelessly transmitted using cloud technology.
IBM's Watson also drew upon this technology when contributing to an Army Stryker "proof-of-principle" exercise last year wherein the service used cloud computing, AI and real-time analytics to perform Conditioned Based Maintenance functions.