JUNE 2021AEROSPACEDEFENSEREVIEW.COM8In My OpinionACHIEVING EFFICIENT AND EFFECTIVE AI-BASED PREDICTIVE MAINTENANCE CAPABILITY--RECOMMENDATIONS FOR MILITARY LEADERSBy Prachi Sukhatankar, VP Technology Solutions & Sandra Burnis-Holly, Director of Business Development/Capture Executive at AmentumIn recent years, transformational technologies like artificial intelligence(AI)-based predictive maintenance have shown the potential to significantly reduce unplanned maintenance, increase asset availability, improve inventory optimization, and reduce total cost of operation in military aviation. The U.S. Department of Defense (DoD) has recognized these technologies to be a game changer. The US Air Force (USAF) embraced this approach through its "Condition Based Maintenance+" (CBM+) concept and planned on tripling data-driven maintenance efforts to create a Predictive Maintenance Analytics Loop(PMAL) which helps identify system failures before they occur, representing a monumental shift away from the "fly to fail" approach. Looking at this transformation continuum in aviation maintenance, we propose how implementations can benefit from a holistic approach involving three key elements: AI-driven cross-correlation of varied datasets,AI-ready knowledge capture and quality assurance,and risk-profile based prioritization.Proactive and predictive maintenance capabilities complement reactive and preventative maintenance programs. Predictive maintenance is now enhanced through AI and machine learning (ML), where self-learning models continually tune themselves to changing conditions over time. For example, analytics of onboard sensor data can detect overspeed events from evasive maneuvers and enable condition monitoring of engine, auxiliary power unit and hydraulic systems, allowing for early, less expensive repairs instead of overhauls or complete failures. AI/ML takes this one step further by detecting complex underlying relationships and identifying failure patterns in components and throughout a fleet--something that would otherwise be difficult to detect. It's an evolution and a paradigm shift from measuring to calculating, from a descriptive to a predictive approach.Justin Smith, Amentum's Executive Director for USAF aircraft maintenance programs and former direct-report to the AFMC Commander, offers a glimpse into a maintenance operation fueled by data-driven insights. "Imagine an algorithm that continually cross-examines F-16 sortie/mission type data from GTIMS and break/maintenance data from IMDS. In short order, this algorithm may find a pattern that mostF-16 ISA breaks occur between 100 and 120 hours of SAT sorties since last Phase. Armed with this information, schedulers can develop a local ISA replacement plan, creating a substantial decrease in ISA breaks."Finally, strategically combining different maintenance programs reduces risk while accounting for differences in asset types, maintenance needs and regulatory requirements. Prachi Sukhatankar
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