United States

In support of developing a credible digital twin for prognostic health management of Navy-relevant equipment, one must first assess the quality of captured data before processing for either digital twin development or health assessment. This paper focuses on assessment of data quality in time-series vibration sensor data. Accelerometers are common in many machinery and equipment health management systems. Sources of poor quality, or corrupted vibration sensor data include internal sensor faults, wiring and connector problems, and incorrect or loose sensor mounting. Other common types of sensors used for health management, such as current or pressure sensors may have similar failure modes. The signal path from measurand to health management processing algorithms are themselves systems that require monitoring and assessment.

Numerous computational techniques exist to varying levels of maturity for monitoring the health of a system from which one can acquire data, including variants of statistical process control and scientific Machine Learning. Those techniques are ubiquitous for assessing equipment health. Whatever the chosen technique to monitor a process, one must curate the data appropriately. In collaboration with the Naval Surface Warfare Center Carderock Division, our team has developed a classifier using machine learning to distinguish between “good” and “bad” data from piezoelectric accelerometers. Algorithms extract salient features from vibration data and predict the class of data quality.

This paper describes the preprocessing, feature extraction and training of the classifier for data quality. We demonstrate our methodology on data sets acquired from machinery on a representative ship platform, from a shaker in a laboratory, and a combination thereof. A secondary goal of the research was to explore the use of simulated sensor data (in this case from a shaker) to supplement the relatively small amounts of actual faulty sensor data. The methodology also includes the use of real modeled sensor data to generate representative laboratory data for model development. This is a common problem in the development of health monitoring systems, for sensor as well as machinery faults. The results show both the ability to detect sensor quality issues and the successful use of supplemental data in model training. 


NAML 2026

Feature Engineering and Classifier Training Method for Vibration Data Quality Assessment

sensor quality

machinery health management

machine learning

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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



Naval operators must execute missions characterized by heterogeneous platforms, contested environments, uncertain sensing, and tightly coupled policy and doctrinal constraints. In contrast, contemporary machine learning ecosystems often presume expert users, well-structured inputs, and narrowly scoped tasks, assumptions that often break down in operational naval contexts. 

This talk introduces LEXICON, an adaptive multi-agent orchestration framework that enables operators to express tasking in natural language. LEXICON leverages a constellation of specialized large language model (LLM) agents to convert these high-level intents into deterministic, verifiable, and auditable workflows that integrate seamlessly with existing planning tools, simulation backends, and retrieval-augmented knowledge sources. The result is a science-driven bridge between unstructured human intent and rigorous algorithmic execution, enabling mission planning pipelines that are transparent, reproducible, and compatible with the Navy’s emerging autonomy and decision-support architectures. 

Key elements include: 
-Multi-agent orchestration with clearly scoped roles wired in a GraphFlow that enforces ordering and termination. 
-A strict data contract and metadata-driven Execution Coordinator that turns REQUIRED_TOOLS lists into deterministic tool runs, with pre- and post-execution gates checking inputs, outputs, and coverage against tool metadata. 
-A retrieval-augmented context layer: document ingestion and embedding build a tagged index; a deterministic DocRetriever agent uses it to enrich context and close gaps, with “doc overrides” merged under explicit rules and checked by the same context-integrity and gating mechanisms as user-provided context. 
-Guardrails including a tool allowlist with an inline JSON to function-call shim that converts agent-emitted call blocks into actual tool invocations, strict versus adaptive execution modes, and a context-integrity policy that prevents agents or RAG from fabricating or silently dropping critical information. 
-Telemetry, tickets, and bounded loopbacks, where detected defects (for example missing geometry or context drift) generate targeted repair requests back to the responsible agent rather than silently continuing. 

We illustrate the approach with Navy-relevant proxy scenarios, including hotspot search and patrol routing with no-fly zones and scan policies, using run logs and state traces to show both successful missions and common failure modes. The talk is organized in two parts: a system overview covering the multi-agent plus RAG architecture and guardrail mechanisms, followed by results and lessons learned from these scenarios—focusing on how to wrap LLM-based agents around legacy tools without violating policy and safety constraints, how much structure data contracts and guardrails need to be useful in naval settings, and what open issues remain for deploying similar orchestration layers in operational environments.

From Intent to Action: Multi-Agent LLMs Coordinating Naval Planning Tools

Ocean surface robotic swarms offer significant potential as reconfigurable landing platforms, ocean monitoring and communication relays. Drawing inspiration from self-assembled chiral crystals formed by starfish embryos, our lab has developed a magnetomechanically actuated spinning robot swarm platform (MASbotS) that self-assembles at the water surface and undergoes controllable phase changes between rigid and fluid states through tuning of interbot repulsion. To explore decentralized control, we apply a reinforcement learning framework in simulation using centralized training with decentralized execution (CTDE) and show that a single shared policy allows a swarm model to navigate to targets with robustness to changes in swarm size, since each agent relies only on local observations. The same approach succeeds in simulated environments with obstacles. To encourage adaptive behavior, we are beginning to coevolve the policy with controlled changes in the simulated environment. Next steps include transferring learned policies to physical MASbotS robots with real-time actuation and sensing, and incorporating feedback to tune swarm interactions for robust operation in real marine settings.

Decentralized Reinforcement Learning for Self-Assembling Ocean Surface Robot Swarms

Genuine human understanding is the underpinning of small and large scale cooperating teams. Understanding is the bridge between information and meaning. It grounds words in perception, actions, and shared values. Without it, communication risks becoming hollow, and trust becomes fragile. While LLMs are evolving with reasoning, planning, tool use and agentic systems to become more valuable at thinking jobs, it is hard to overlook the fact that they do not truly understand like humans. Compounding the problem, human users anthropomorphize AI systems, leading to over trust in long-term collaborations.

In this paper we examine ways in which LLMs can be evaluated for human like understanding even on tasks they complete satisfactorily. Based on that we postulate that the gap in human and LLM understanding engenders a critical barrier for long running human machine teams especially in high stakes domains. We argue that this gap cannot be bridged by improving LLM reasoning alone. 

In view of this, any human-machine long running system should be designed to explicitly account for this disconnect by: avoiding a blackbox paradigm (opaque understanding and uncertainties in LLMs), creating a calibrated trust design (such as using confidence scoring), formalizing human oversight and grounding (mandatory review gates) and creating designs for context aware interaction (that explicitly overcome misinterpretation).

We propose a framework of principles for high-stakes human machine team collaboration system design to help narrow the gap:
1. Mode - combine LLMs with causal reasoning systems to reduce hallucinations and improve consistency 
2. Interaction - leverage explainable AI techniques such as contrastive explanations and traceability
3. System - treat LLM as tools, not as teammates with precise roles

The immediate goal is not to build LLMs that "understand like humans," but to design systems that compensate for their limitations while leveraging their strengths. However, we do recognize that the LLMs that exhibit human like understanding might arrive in future.

The Understanding Gap: Managing Risk in Human-LLM Collaboration When Reasoning Is Not Enough

The adoption of agentic AI is a high-priority topic for the Department of War, offering the potential to dramatically accelerate the development of complex systems like Command and Control (C2). However, this potential is balanced by significant risk. The core question for Naval leadership is no longer if to use agentic AI, but how to control it. Un-governed agents introduce profound security, supply chain, and reliability risks, from model "hallucination" and prompt injection vulnerabilities to insecure code generation. These risks are unacceptable for mission-critical systems that demand the high levels of trust central to the NAML mission. 

This presentation details a "dual-control" DevSecOps framework that provides the necessary governance to safely harness agentic AI. This pattern is aligned with the DoW DevSecOps Reference Design and lessons from initiatives like Platform One and NIWC's Overmatch Software Armory. First, we govern the agent's inputs by making it context-aware. The agent is constrained by ingesting a rich, domain-specific context from hardened, pre-configured application templates that programmatically codify DoW-mandated security controls (e.g., NIST 800-53). Second, we govern the agent's outputs by subjecting all AI-generated code to a rigorous, automated CI/CD pipeline. This pipeline validates the agent's output via policy-as-code enforcement (e.g., OPA/Kyverno), comprehensive security scanning, and GitOps promotion gates. 

The auditable result of this governed framework is a complete, machine-readable compliance package. The pipeline automatically generates: 1) A Software Bill of Materials (SBOM) , enabling continuous supply chain vulnerability monitoring per EO 14028. 2) A CI-generated Body of Evidence (BoE) , which provides the verifiable artifacts and evidentiary foundation required to rapidly author the formal RMF documents (SSP, SAR, POA&M). This approach is portable across Naval cloud environments and disconnected tactical edge targets like ships and autonomous platforms. 

This "dual-control" framework provides a tangible methodology for building trust in AI-generated systems. It directly addresses the RMF/ATO bottleneck as a byproduct of solving the more critical AI governance problem. This enables the technical foundation for Continuous Authorization to Operate (cATO) , allowing the DoN to move trusted C2 and AI-driven capabilities from "lab-to-fleet" at the speed of relevance.

Governing Agentic AI: A DevSecOps Control Framework for Trusted Naval C2 Systems

My team at the University of Arizona’s SpaceTREx Laboratory has been working extensively with planetary scientists on advancing spacecraft technologies for high-priority reconnaissance missions. Among them are plans for fast flyby of small bodies, moons, and interstellar objects. For these application areas, we have been proposing spacecraft swarms that fly by a body or inspect it, providing rapid 3D mapping and imagery. The organization and coordination of these swarms are achieved through the use of Machine Learning algorithms that evolve the craft design, swarm organization, and trajectories of multiple crafts. Building on these flyby mission concepts, we are beginning to explore alternative techniques, including rendezvous, impactor/ramming, and landing/docking. Many of these architectural and operational techniques may be applicable for robotic UUVs. Using a swarm or fractionated modular craft, we may split groups depending on needs to take on specialized tasks, including maintaining communications links, inspecting a craft, or performing analysis from a complex debris field. Given a target, maybe an uncooperative craft, one or a few may get close, but others in the swarm would maintain a safe distance. If the craft is uncooperative and is on a collision course with vital assets, one consideration may be to use a ramming spacecraft that impacts and conjoins with the uncooperative craft and moves it off onto a safe trajectory. In other examples, we have used a spacecraft swarm to perform multipoint observation of a complex 3D event without any craft being a single point of failure for the evolving mission. These projects are early-stage and currently supported by NASA and Brinson Foundation funding. We have made promising progress in simulation and are planning towards an early-stage hardware demonstration in the laboratory through external support in preparation for field demonstrations.

Advancing Machine Learning Directed Design and Organization of Spacecraft Swarms Concepts Towards Terrestrial and Underwater Applications

Maritime vessel controls and operational technology (OT) systems are getting more complex and interconnected. With industry trends aiming to reduce crew, automate tasks, and improve efficiency, these networks are expanding in scale, intricacy, and criticality for vessel operation and maintenance. The standard controller area network (CAN) bus for maritime vessel networks, developed by the National Marine Electronics Association (NMEA), known as NMEA2000. NMEA2000 is an application layer network protocol built on the ISO11783 standard and compatible with automotive SAEJ1939, it uses unique message identifiers known as Parameter Group Number, to define the data within each communication frame.

Despite its widespread use, NMEA2000 remains a relatively unexplored domain, particularly in understanding normal versus abnormal network behavior, due to the unavailability of open-source datasets. To address this gap, we constructed a NMEA2000 system consisting of five nodes: GPS/Radar, Wind Speed/Direction sensor, and Multifunction Display. Using this setup, we collected datasets to analyze system behavior and developed an intelligent fingerprinting techniques for each sensor, establishing a baseline of the normal operating system. We subject the system to controlled attacks to evaluate the accuracy and effectiveness of the fingerprints. This work represents a foundational step towards enhancing security and reliability in maritime OT systems.

Intelligent NMEA2000 Network Fingerprinting

The Citizen Broadband Radio Service (CBRS) utilizes the 3.55-3.7 GHz band for dynamic spectrum sharing, enabling commercial cellular network operations without causing harmful interference to high-priority Navy incumbent systems. This protection is facilitated by a dedicated Environmental Sensing Capability (ESC) that continuously monitors for incumbent activity and triggers network responses. As spectrum sharing extends to additional frequency bands beyond CBRS, augmenting dedicated sensing with complementary sensing modalities may be necessary for enhancing detection performance and response times. 
This presentation proposes a novel approach for sensing that leverages commercial cellular base stations as distributed sensors to detect incumbent spectral utilization. The approach employs the You Only Look Once (YOLO) real-time object detection architecture applied to a time–frequency representation (spectrogram) of received signals. YOLO models are trained on a combination of synthetic and measured radar datasets that encompass diverse radar types, pulse structures, and signal-to-noise ratios (SNRs). The framework simultaneously detects radar emissions and classifies their type, enabling efficient and reliable identification of incumbent activity.
In contrast to signal processing and classical machine learning methods that rely on handcrafted features and fixed detection thresholds, the proposed deep learning pipeline adapts to complex, nonstationary environments and maintains high detection performance across varying SNRs and interference conditions. The sensing capability is validated using a laboratory testbed comprising an NVIDIA Aerial Platform-based 5G base station with a GPU-accelerated physical layer integrated with the Open-Air Interface (OAI) software stack. Radar waveforms specified by the National Telecommunications and Information Administration (NTIA) for the ESC certification were utilized for performance testing. Executing advanced machine learning (ML) inference on a GPU-powered base station yields end-to-end latencies that are significantly better than current CBRS deployments. For this study, we target a chirp radar waveform as specified by NTIA for ESC certification. Our YOLO-based radar detector achieves approximately 98% precision and 95% recall at moderate-to-high SNRs, with sub-10 ms ML model inference per spectrogram. The improved sensing performance enables commercial networks to initiate timely mitigation actions that can protect incumbent systems while maintaining the cellular communication quality of service.
The proposed YOLO-based distributed sensing framework demonstrates that commercial 5G base stations can serve as efficient, real-time spectrum sensors, achieving improved detection accuracy and low latency. The experimental results establish deep learning–based sensing and AI-driven spectrum agility as a practical foundation for next-generation dynamic spectrum sharing systems. 


AI-Enabled Real-Time Sensing Using 5G Base Stations for CBRS Spectrum Sharing

Emerging GenAI software developer tools (e.g. Claude Code, Codex, Antigravity, etc.) in industry are transforming what it means to be a software engineer. However, current enterprise GenAI providers (NIPRGPT, CapraGPT, DoD HPC, etc.) across the DoD have multiple gaps, including limited application programming interface (API) access to large language models (LLMs) and the supporting infrastructure required to share agentic workflows across the enterprise. 

In this talk, the Naval Information Warfare Center (NIWC) Atlantic's Generative AI for Software Development (GenAI4SW) team investigates how to affordably scale AI tool deployments from the individual to the team level. As part of our efforts, we examine the infrastructure and workforce development strategies required to share LLM endpoints, build agentic workflows, and debug LLM-based applications. Our experiences highlight how GenAI tools are leveraged at a Navy Warfare Center and highlight grassroots efforts surrounding AI-enabled software engineering best-practice. 

As part of this effort, the team deployed multiple 49-billion parameter reasoning models across the DoD High Performance Computing Modernization Program (HPCMP) and shared OpenAI-compliant APIs that could be integrated into software development environments across the team. Our contributions are extended to included: (1) an architecture for sharing Model Context Protocol (MCP) servers to enable LLMs to call external tools, (2) observability frameworks to enable application debugging with LLMs, and (3) human-centered interfaces for building (e.g., n8n) an interacting with (e.g., Open WebUI) LLM-based applications. 

Our efforts are designed to enable and advertise the use AI tools across the software development lifecycle, influence enterprise adoption of these workflows, and demonstrate capabilities of existing computational infrastructure for GenAI within the DoD.

Generative AI Infrastructure for Software Development

Understanding the state of a given workforce is a prevalent issue across commands and organizations. One specific difficulty is predicting and planning for attrition. Knowing who and when an employee will attrit is paramount to maintaining a healthy workforce and ensuring ample time for transition periods between new and more senior employees. Predicting attrition, both on the individual and command level, is often a challenging problem to solve due to high class imbalance and data scarcity. Additionally, organization managers may be resource constrained, limiting the time available to parse through large candidate pools and find ideal candidates to backfill attrition-based losses. The proposed solution is two-fold: a machine learning (ML) based attrition prediction model and a novel candidate recommendation system. For attrition prediction, multiple custom models were developed, trained, and tested to compare and validate prediction performance including Deep Neural Networks (DNNs) and Convolution Neural Networks (CNNs) for individual predictions as well as Long Short-Term Memory (LSTM) models for command-level predictions. A suite of oversampling and feature engineering tools were employed in conjunction with the ML models. An explainability study was conducted for individual attrition predictions using SHapley Additive exPlanations (SHAP) to provide traceability between individual features and model outputs. For the candidate recommendation system, an ML-based resume intake pipeline was developed for reviewing resumes and evaluating job applicants for positions in specific command knowledge areas (KAs). Given the KA of an open position, the system recommends the top candidate matches using the skills extracted from their resume via a locally-hosted Large Language Model. Top-matching applicants are determined by leveraging vector embedding to compute a match score for each (KA, applicant) pair, where the match score quantifies the weighted cosine similarity between an applicant's skills and the KA's description and job-specific skills. The system concisely returns each applicant's most position-relevant skills for reference alongside the match score. The system output allows hiring managers to spend less time scanning through applicant resumes, and more time reviewing those applicants with relevant skills, resulting in better quality matches between candidates and job requirements. Both the attrition prediction models and the candidate recommendation system provide Naval Surface Warfare Center – Philadelphia Division with enhanced tools for business analytics and organizational needs.

Artificial Intelligence and Machine Learning for Business Analytics

Technology Goddesses™️was founded in 2002 as a volunteer-led 501.c.3 to improve the representation of diverse people in STEM degrees and occupations. This poster shares some of the metrics that need to change and how our approach to STEM education is different than many other programs.

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