The U.S. Air Force is to add a dozen weapons systems to its Enhanced Reliability Centered Maintenance (ERCM) model that employs artificial intelligence/machine learning (AI/ML) for predictive maintenance.

Those systems are the Boeing [BA] F-15 fighter, B-52 bomber, RC-135 reconnaissance plane, C-17 transport, and A-10 Thunderbolt II close air support aircraft, the Lockheed Martin [LMT] AC/MC-130 gunships, F-16 fighter, and HH-60 helicopter, the Bell [TXT] and Boeing CV-22 tiltrotor, the Northrop Grumman [NOC] RQ-4 Global Hawk and the General Atomics‘ MQ-9 Reaper.

“We have a couple of different initiatives under what we would call the umbrella of predictive maintenance,” Air Force Lt. Gen. Warren Berry, the service’s deputy chief of staff for logistics, engineering and force protection, said during a July 9 Mitchell Institute for Aerospace Studies’ Aerospace Nation virtual discussion. “One is Condition Based Maintenance Plus [CBM+]. We have three weapons systems in there right now: the C-5, the KC-135, and the B-1. They’ve been doing it for about 18 to 24 months now, and we’re starting to get some real return on what it is that the CBM+ is offering us. The other element is called Enhanced Reliability Centered Maintenance [ERCM], which is really laying that artificial intelligence and machine learning on top of the maintenance information system data that we have today and understanding failure rates and understanding mission characteristics of the aircraft and how they fail, and then laying that into the algorithms that then tell us when parts are likely to fail based on failure rates and the algorithms we plug in.”

“We’re in the process of adding another 12 weapons systems under the ERCM umbrella this calendar year,” Berry said. Defense Daily has asked Air Force Materiel Command (AFMC) for the names of the 12 systems.

AI/ML is to assume a significant role in predictive maintenance for the 11 combatant commands (COCOMs).

In April last year, the Pentagon said that the new Joint Artificial Intelligence Center (JAIC) had delivered its first product, a predictive Engine Health Model (EHM) maintenance tool for Sikorsky [LMT] Black Hawk helicopters, to U.S. Special Operations Command’s 160th Special Operations Regiment (SOAR) for use with SOAR’s MH-60 helicopters.

JAIC said that its Joint Logistics Mission Initiative (MI), one of six JAIC AI projects, is working “to develop a repeatable, end-to-end AI ecosystem” to bring EHM to scale across the Black Hawk fleet.

EHM, developed in partnership with Carnegie Mellon University, “predicts the probability of an engine hot start so decision-makers can consider next steps,” including replacing the engine or holding it back for training missions instead of deployments in high-risk missions, Army Col. Kenneth Kliethermes, JAIC’s Joint Logistics MI lead, said in a recent JAIC blog post.

Another JAIC mission initiative, the Joint Warfighting MI, “is working with several COCOMs to build, test, and expand its Smart Sensor, a video processing AI prototype that rides on unmanned aerial vehicles and is trained to identify threats and immediately transmit the video of those threats back to manned computer stations for real-time analysis,” according to the JAIC blog post.

Army Col. Bradley Boyd, the lead for the Joint Warfighting MI, said that the Smart Sensor could lead to “a dramatic reduction in the amount of data that has to be pushed back for a human to cull through.”

“Instead of staring at one video feed and hours and hours of trees and rocks and nothing happening, that person can instead be monitoring 10 video feeds because they are only seeing the stuff that really matters,” Boyd said in the JAIC blog post.