JUNE 2021AEROSPACEDEFENSEREVIEW.COM9As the DoD recognizes the need for predictability, increased speed and enhanced readiness, solutions must shift to gain a `decision advantage'.It is important to define critical characteristics that would underpin a holistic approach to next-gen AI/ML solutions.Using AI-driven Cross-Correlation to Analyze Varied DatasetsTraditional predictive maintenance technologies rely on equipment conditions to predict maintenance requirements, which require configured instrumentation such as sensors. AI/ML techniques powered by scalable computing allow for identification of associations in non-sensor datasets such as those housed in maintenance and supply management information systems. AI/ML techniques can aid predictions by identifying correlations between a large number of conditions. In an aviation maintenance domain, these could range from aircraft components and characteristics, flight hours, sortie/mission types, failure data including "mean time to failure" indicators, quality assurance data, pilot/maintainer profiles, environmental conditions and more. While this allows us to extract previously unknown insights, it also allows the sustainer to more effectively manageolder, simpler platforms where onboard data is not available.The Defense Innovation Unit reported that recent USAF implementations of AI-based approaches demonstrated the potential for a 3 to 6 percent improvement in mission capability; up to a 35 percent reduction of base-level occurrences of grounded aircraft awaiting parts; and upto a 40 percent reduction in unscheduled maintenance events.Capturing Meaningful AI-Ready Data at the Right Place, Right TimeAI systems can be significantly enhanced by capturing more meaningful data at the point of maintenance. In an aviation maintenance environment, inefficiencies and risks arise when maintenance knowledge resides within a small group of experts and is not fully captured or transferred to others. This not only reduces the level of shared understanding but also ultimately impacts operational readiness. It is essential that maintainers consistently, accurately, and comprehensively document discrepancies, flight configuration (e.g., flaps up, spoilers retracted), and anomalies in maintenance management systems. For example, rather than "the hydraulic pump is leaking" or "engine vibes high," descriptions that will yield more useful insight would be "primary hydraulic pump is leaking from the pressure side 90-degree elbow" and "engine vibes were at the 1.0 IPS limit during transition from start to ground idle." Training and quality assurance efforts are necessary to build this into the culture. In fact, AI-based approaches can enable identification of these gaps in data quality, which when mitigated can significantly enhance training as well as compliance.Further, as we encourage better capture of knowledge, it is equally important to extract that knowledge from free-form text fields in in maintenance management systems. Natural language processing approaches (a subset of AI)allow extraction and must be integrated into the overall solution. This would, in turn,aid verification of diagnostics and enable cross-domain knowledge transfer. These solutions also surface the gaps in captured knowledge, thus acting as a feedback loop to improve data quality, ultimately bolstering the effectiveness of the predictive AI/ML models.Scaled, Risk-based, and Agile ApproachWhile initial solutions using AI-based approaches have shown significant potential, the challenge of scaling these efforts is exacerbated by large volumes of data produced by varied systems and the timeliness with which the information needs to be collected, disambiguated, analyzed and presented. Developing sustainable AI-driven predictive analytics solutions requires time,expertise, and if not carefully developed, significant up-front investment. Taking an agile development approach that is incremental and iterative, prioritizing efforts by keeping in mind the mission relevance and risk profile and adding security controls upfront will be key to the overall success of these programs.Reducing maintenance time and costs long-term, improving speed and relevance of analysis, and ultimately achieving higher operational readiness requires a holistic approach that looks at not just maintenance approaches and AI algorithms, but also includes data lifecycle management, critical infrastructure view, and upfront operational/risk considerations.The best solutions are those that complement existing expertise, helping humans understand the tangible, while machines sift through the intangible, transforming bits and bytes into information and knowledge that help forge the edge for tomorrow's war fighters.Prachi Sukhatankar is Vice President of Technology Solutions, and Sandra Burnis-Holly is Director of Business Development for Amentum, a premier global technical and engineering services partner supporting critical programs of national significance across defense, aviation, security, intelligence, energy, and environment. Sandra Burnis-Holly
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