Browse Topic: Systems engineering
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
Reliable antenna performance is crucial for aircraft communication, navigation, and radar detection systems. However, an aircraft's structure can detune the antenna input impedance and obstruct radiation, creating a range of potential problems from a low-quality experience for passengers who increasingly expect connectivity while in the air, to violating legal requirements around strict compliance standards. Determining appropriate antenna placement during the design phase can reduce risk of costly problems arising during physical testing stages. Engineers traditionally use a variety of CAD and electromagnetic simulation tools to design and analyze antennas. The use of multiple software tools, combined with globally distributed aircraft development teams, can result in challenges related to sharing models, transferring data, and maintaining the associativity of design and simulation results. To address these challenges, aircraft OEMs and suppliers are implementing unified modeling and
This paper presents the development of an alternative to the traditional multichannel Fiber Optic Rotary Joint (FORJ) using spatial division multiplexing. The proposed solution utilizes phase plates assembly in a compact housing made by a French optical communications company called Cailabs. It is distinguished from conventional multichannel technologies that rely on Dove prisms or wavelength multiplexing by using the housing of a single channel Fiber Optic Rotary Joint (FORJ) without needing strong constraint on the choice of optical transceivers. Our research focused on characterizing the specific mechanical parameters required to transfer optical modes from the rotor to the stator without deformation or misalignment of those. Three test campaigns were conducted, each with iterative improvements. The latest results demonstrate commercially viable performance for transmission of 3G-SDI video stream on up to 6 channels.
To achieve Army modernization plans, advanced approaches for testing and evaluation of autonomous ground systems and their integration with human operators should be utilized. This paper presents a framework for developing digital twins at the subsystem level using heterogeneous modeling and simulation (M&S) to address the challenges of manned-unmanned teaming (MUM-T) in operational environments. Focusing on the interplay between robotic combat vehicles (RCVs) and human operations, the framework enables evaluation of soldiers’ cognitive loads while managing tasks such as maneuvering robotic systems, interacting with aided target detection, and engaging simulated adversaries. By employing subsystem-level digital twins, we aim to isolate and control key variables, enabling a detailed assessment of both systems’ performance and operator effectiveness. Through realistic operational scenarios and human-machine interface testing, our approach may help identify optimal solutions for soldier
The increasing complexity of systems has necessitated a modernized model-centric approach to design them. Becoming fully model-centric has introduced a new set of challenges that need to be overcome in order to realize the full potential from this new approach. This paper presents a plugin for Cameo System Modeler 2022x that automates the extraction of SysML Block Definition Diagram data from an entire model or a selected diagram. The extracted data is formatted into JSON and processed via a Java-based API client, which sends it to Mistral AI for interpretation. The AI-generated textual summary provides insights into system components and relationships, streamlining model comprehension and decision-making. By integrating AI-driven interpretation into the Cameo environment, this approach enhances model-based systems engineering (MBSE) workflows, reducing the manual effort required to analyze complex architectures. The paper discusses the plugin’s implementation, its benefits in model
This paper presents a model-based systems engineering (MBSE) and digital twin approach for a military 6T battery tester. A digital twin architecture (encompassing product, process, and equipment twins) is integrated with AI-driven analytics to enhance battery defect detection, provide predictive diagnostics, and improve testing efficiency. The 6T battery tester’s MBSE design employs comprehensive SysML models to ensure traceability and robust system integration. Initial key contributions include early identification of battery faults via impedance-based sensing and machine learning, real-time state-of-health tracking through a synchronized virtual battery model, and streamlined test automation. Results indicate the proposed MBSE/digital twin solution can detect degradation indicators (e.g. capacity fade, rising internal impedance) earlier than traditional methods, enabling proactive maintenance and improved operational readiness. This approach offers a reliable, efficient testing
This paper explores the integration of Microsoft Power BI into Model-Based Systems Engineering (MBSE) workflows, specifically within a Model-Based Product Line Engineering (MBPLE) context. Power BI provides a versatile platform for visualizing, analyzing, and manipulating data, enabling users to configure system variants outside traditional MBSE environments while maintaining integration back into the original MBSE model. This approach enhances collaboration between engineering and business disciplines, improves decision-making with real-time data analysis, and allows users to configure and evaluate multiple system variants efficiently. Additionally, the paper discusses how Power BI’s interactive dashboards facilitate better accessibility and analysis, bridging the gap between technical teams and non-technical stakeholders. Future work will focus on improving data pipeline automation and incorporating feature performance metrics to enable real-time trade study analysis, further
The Model-Based Systems Engineering and Software Engineering (MB(SE)2) capability aims to seamlessly integrate systems engineering and software (SW) development. This approach leverages advanced modeling tools, issue tracking systems, and a continuous integration/continuous delivery (CI/CD) toolchain to align SW development with system requirements and design specifications. MB(SE)2 enhances communication, efficiency, and adherence to specifications by automating model updates and integrating various tools throughout the development lifecycle. This improves the overall quality and reliability of developed systems, making it a valuable asset for organizations focused on delivering high-quality SW solutions. Additionally, MB(SE)2 facilitates better collaboration between cross-functional teams, reduces the risk of errors and inconsistencies, and accelerates the development process. By providing a unified framework for managing systems engineering and SW development activities, MB(SE)2
The objective of this paper is two-fold. Firstly, provide guidance to best implement end to end traceability from program requirements to physical implementation, and Secondly provide techniques to review and understand large scale complex systems. Even with a Digital Engineering Environment (DEE) being an enabler towards applying Systems Engineering practices to develop large scale complex systems, many organizations are unclear on the methodology for modeling their architectures and enabling stakeholders to easily review, understand and assess those architectures. An architecture can be a conceptual, logical or physical architecture, depending on the system’s lifecycle state. For the context of this paper, the modeling environment is any System’s Modeling Language (SysML) based tool along with modeling tools for electrical, mechanical and software development and product life cycle management tool. The intended audience is any engineering organization defining end-to-end architecture
The integration of digital twins within a digital thread framework offers significant benefits for managing Army ground and surface water vehicles. This paper examines how digital twins can enhance lifecycle management, operational efficiency, and maintenance for mature and new military vehicle programs. Scalable and cost-effective implementation with layered capabilities allows organizations to start with a cost-effective foundational model and phase in additional layers of capability over time. This phased approach allows you to expand your digital twin capabilities as program budgets permit, ensuring that you can adapt to evolving requirements without overwhelming upfront investment. For established programs, digital twins enable real-time monitoring, predictive analytics, and data-driven decisions, improving resource allocation and cutting costs. For new programs, they speed up prototyping, integrate modern technologies, and enhance training capabilities. Case studies demonstrate
While the Department of Defense’s transition to model-based deliverables promises numerous benefits, it presents a formidable challenge for acquisition program offices struggling to acquire the requisite skill sets. A critical deficiency in experience with Systems Modeling Languages (e.g., SysML) and essential modeling tools (e.g., Cameo Systems Modeler) has resulted in a preference for traditional document-based deliverables. This paper explores how Model-Based Systems Engineers can address this gap by leveraging data-driven insights to support design reviews and enhance stakeholder communication. To overcome the challenge of limited Model-Based Systems Engineering expertise, we introduce a model-based design review tool that simplifies complex vendor system architecture models, making the information readily usable for Subject Matter Experts. The tool’s ”indirect commenting method” and heuristics facilitate effective model evaluation and increase confidence in vendor designs beyond
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