Model-based Engineering Approach Research Articles

A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture

In product development, important specification and design decisions must be made at various stages of the lifecycle that include design, manufacturing, operations, and support. However, making these decisions becomes more complex when a multi-disciplinary team of stakeholders is involved in system-level or subsystem-level architecture and design decisions. Model-Based Engineering (MBE) approaches are enabling a digital thread of connected data and models. This work demonstrates a novel MBE approach that incorporates a model-based systems engineering (MBSE) method and a multi-criteria decision-making (MCDM) method to determine the best architecture solution that aligns with stakeholder needs and objectives over multiple domains. This approach demonstrates the connection of a system descriptive model, modeled using the systems modeling language (SysML), to underlying physics-based engineering models for the purpose of better predicting the technical performance of systems during the architecture development phase. This approach is demonstrated for a common aerospace communications application, a software-defined radio. This novel MBE approach supports digital transformation at organizations and allows for earlier design validation, enabling designers to test and select the best system architecture from many candidates and validate that the design meets stakeholder needs.

An approach for ontology-based research and recommendation on systems engineering projects

Recommendation systems (RS) have dramatically evolved these past few years, generating more and more accurate results and developing innovative filtering methods. A huge breakthrough was the integration of ontologies into their recommendation process. It integrated the domain's knowledge into its reasoning process, thus overcoming the limitations of the conventional RS. The main purpose of this paper is to explore the application of ontology-based RS within critical infrastructure projects implementing the systems engineering or model-based engineering approach.

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Maintaining safe operations in cyber physical systems is a complex task that must account for system degradation over time, since unexpected failures can result in the loss of life and property. Operational failures may be attributed to component degradation and disturbances in the environment that adversely impact system performance. Components in a CPS typically degrade at different rates, and, therefore, require continual monitoring to avoid unexpected failures. Moreover, the effects of multiple degrading components on system performance may be hard to predict. Developing and maintaining accurate physics-based system models can be expensive. Typically, it is infeasible to run a true system to failure, so researchers and practitioners have resorted to using data-driven techniques to better evaluate the effect of degrading components on overall system performance. However, sufficiently organized datasets of system operation are not readily available; the output of existing simulations is not organized to facilitate the use of data-driven machine learning techniques for prognostics. As a step toward addressing this problem, in this paper, we develop a data management framework and an end-to-end simulation testbed to generate such data. The framework facilitates the development and comparison of various system-level prognostics algorithms. We adopt a standard data-centered design methodology, combined with a model based engineering approach, to create a data management framework that address data integrity problems and facilitates the generation of reproducible results. We present an ontological design methodology centered around assets, processes, and data, and, as a proof of concept, develop an unmanned aerial vehicle (UAV) system operations database that captures operational data for UAVs with multiple degrading components operating in uncertain environments. Aim: The purpose of this work is to provide a systematic approach to data generation, curation, and storage that supports studies in fault management and system-level prognostics for real-world and simulated operations. We use a data-driven simulation-based approach to enable reliable and reproducible studies in system-level prognostics. This is accomplished with a data management methodology that enforces constraints on data types and interfaces, and decouples various parts of the simulation to enable proper links with related metadata. The goal is to provide a framework that facilitates data analysis and the development of data-driven models for prognostics using machine learning methods. We discuss the importance of systematic data management framework to support data generation with a simulation environment that generates operational data. We describe a standard framework for data management in the context of run-to-failure simulations, and develop a database schema and an API in MATLAB® and Python to support system-level prognostics analyses. Methods: A systematic approach to defining a data management framework for the study of prognostics applications is a central piece of this work. A second important contribution is the design of a Monte Carlo simulation environment to generate run to failure data for CPS with multiple degrading components. We adopt a bottom-up approach, starting with requirements and specifications, then move into functionality and constraints. With this framework, we use a Monte-Carlo simulation approach to generate data for developing and testing a variety of system-level prognostics algorithms. Results: We have developed a data management framework that can handle high dimensional and complex data generated from real or simulated systems for the study of prognostics. In this paper, we show the advantages of a well-organized data management framework for tracking high-fidelity data with high confidence for complex, dynamic CPS. Such frameworks impose data logging discipline and facilitate downstream uses for developing and comparing different data-driven monitoring, diagnostics, and system-level prognostics algorithms. Conclusions: In this paper, we demonstrate the design, development, and use of an asset, process, and data management framework for the research to develop prognostics & health management applications. This work helps fill a gap for system-level remaining useful life studies by providing a comprehensive simulation environment that can generate run to failure data, and a data management architecture that addresses the needs for system-level prognostics research. The framework is demonstrated with a Monte-Carlo simulation of a UAV system that operates multiple flights under different environmental conditions and degradation sources. This architecture for data management will enable researchers to conduct more complex experiments for a variety of cyber physical systems applications.

Mining CSTNUDs significant for a set of traces is polynomial

A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism for temporal plans that models controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds CSTNUDs to model, validate and execute some temporal plans of interest. In this paper, we investigate a bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). We provide a prototype implementation and we test it with a set of artificial data. Finally, we elaborate on consistency and controllability of mined networks.

Smart City Solution Engineering

Many smart city applications have been proposed and demonstrated over the years; however, moving to large-scale deployment is still scarce. A contributing factor to this scarcity is the lack of well-established engineering methodologies for large-scale smart city applications. This paper addresses engineering methodologies and tools for large-scale smart city application engineering, implementation, deployment, and evolution. A model-based engineering approach based on IoT, SOA, and SysML is proposed and applied to a smart streetlight application. Engineering considerations for streetlight area enlargement and updated technology generations with additional capabilities are discussed. The proposed model-based engineering approach provides considerable scaling simplifications and opportunities for considerable savings on engineering costs. The model-based engineering approach also provides good documentation that enables technology evolution specifications that support both maintenance and emerging behaviours.

Modeling for Rapid Systems Prototyping: Hospital Situational Awareness System Design

The COVID-19 pandemic caught hospitals unprepared. The need to treat patients remotely and with limited resources led hospitals to identify a gap in their operational situational awareness. During the pandemic, Israeli Aerospace Industries helped hospitals to address the gap by designing a system to support their effective operation, management and decision making. In this paper, we report on the development of a functional, working prototype of the system using model-based engineering approach and tools. Our approach relies on domain-specific modeling, incorporating metamodeling and domain-specific representations based on the problem domain’s ontology. The tools practiced are those embedded into the Eclipse Modeling Framework—specifically, Ecore Tools and Sirius. While these technological tools are typically used to create dedicated, engineering-related modeling tools, in this work, we use them to create a functional system prototype. We discuss the advantages of our approach as well as the challenges with respect to the existing tools and their underlying technology. Based on the reported experience, we encourage practitioners to adopt model-based engineering as an effective way to develop systems. Furthermore, we call researchers and tool developers to improve the state-of-the-art as well as the existing implementations of pertinent tools to support model-based rapid prototyping.

A model-based approach for design and verification of Industrial Internet of Things

This investigation presents an Industrial Internet of Things (IIoT) architecture and a Model-Based Engineering (MBE) approach for design, verification, and auto-code generation of control applications in process industries. The IIoT architecture describes the hardware components, communication modules, and software. It emerges as a major enabler for providing open connectivity to process industry which provides greater data-aggregation, visibility, availability, flexible control, and cloud-connectivity. The MBE approach is based on multiple views of the systems with each domain model describing a particular view. The multi-view modelling approach is used to perform design and verification of the IIoT enabled control in process industries. We show that such an integration of MBE, cloud-computing, and IIoT provides certain desirable features such as plug-and-play control and on-the fly verification which are lacking in the process industry. The proposed MBE approach and IIoT architecture are illustrated on the quadruple tank process, a benchmark problem in control. Our deployment results verify the benefits envisaged by IIoT, cloud, and MBE integration.

Towards verified continuous integration in the engineering of automated production systems

Abstract Continuous integration (CI) is widely used in software engineering. The observed benefits include reduced efforts for system integration, which is particularly appealing for engineering automated production systems (aPS) due to the different disciplines involved. Yet, while many individual quality assurance means for aPS have been proposed, their adequacy for and systematic use in CI remains unclear. In this article, the authors provide two key contributions: First, a quality model for a model-based engineering approach specifically developed for aPS. Based thereon, a discussion of the suitable verification techniques for aPS and their systematic integration in a CI process are given. As a result, the paper provide a blueprint to be further studied in practice, and a research agenda for quality assurance of aPS.

Model-Based Engineering for Water Management in the Gas Diffusion Layer of PEMFC

The water management in the membrane electrode assembly (MEA) is one of the most critical factors for both performance and durability. Operating conditions and material properties of gas diffusion layer (GDL) should be optimized to provide proper water management in the MEA. Some experimental investigations such as synchrotron x-ray computed tomography (CT) and neutron imaging have been studied to visualize two-phase thermodynamic fluid in the MEA. However, model-based engineering capability is significantly efficient to optimize the two-phase water management and hydration control. Conventional fuel cell models count the water saturation in the GDL with Darcy’s law and define water saturate as uniform inside the GDL, although it is varied with locations in the real system. The simulations with such a homogeneous model sometimes underestimate the effect of mass transport overpotential in the GDL [1]. A two-phase calculation technique that incorporates detailed micro-scale structure of the GDL was developed with Lattice Boltzmann Method (LBM) and validated with experimental data of water breakthrough pressures [2]. This direct GDL model with LBM was replaced with the homogeneous GDL model in the computational fluid dynamics (CFD) based fuel cell performance model [3, 4]. Simulations of fuel cell performance with this integrated model show better matching with the performance data for wet and dry conditions respectively, although simulations from the conventional homogeneous model cannot match the performance of dry and wet conditions (Fig. 1). This integrated model can properly estimate the effect of water saturation in the GDL. A model-based engineering approach is explored with this integrated model to optimize water management in the MEA. Simulations of fuel cell performance are pursued with various thermal conductivities of micro porous layer (MPL) in the GDL. The GDL with higher thermal conductivity MPL shows higher water evolution inside the GDL and resulted in lower oxygen concentration at the border between GDL and cathode catalyst layer. Figure 2 shows the temperature and water saturation profiles in the GDLs with various thermal conductivities. Temperature of higher thermal conductivity MPL is lowered and it causes higher evolution of water. Fuel cell performance of this higher thermal conductivity MPL in the GDL shows the highest among three cases although the lowest oxygen concentration at the cathode catalyst layer. The model prediction is extended to the hydration of the membrane of each thermal conductivity cases. The results clearly show that higher thermal conductivity GDL makes higher membrane hydration and higher proton conductivity which overcomes mass transport loss due to lowered oxygen concentration at the cathode catalyst layer. This work can be extended to water management with various operating conditions to show capability of the model-based engineering.

Automatic analysis and adaption of the interface of automated material flow systems to improve backwards compatibility

Today’s industrial automation production systems (aPS) have to deal with the requirement to produce many different products. Hence, not only a flexible modularized aPS, but also a flexible modularized material flow system (MFS) is required in order to improve modifications to the system, i.e. extending, reducing or modifying parts of the system. To reduce the engineering effort involved in setting up modularized MFS and to deal with the complexity of the system, a definition of the architecture, behavior and (pre)defined interfaces of such modules is provided by a model-based engineering approach, which is based on an appropriate meta model. In addition, the modules can interact with neighboring modules to execute specific logistic functionalities by using (pre)defined interfaces. Since the development of automated material flow modules (aMFM) can be considered as an evolutional process, the aMFM are frequently redeveloped using current-state-of-the-art technologies, which, in some circumstances, also require newly-developed interfaces. Thus, an inconsistency between older and newer modules containing different version numbers occurs when different (pre)defined interfaces are used, leading to a reduction in system flexibility. To implement the backward compatibility of newer modules, the (pre)defined software interfaces of the newer modules, or even of its neighboring modules, usually have to be adapted manually, which increases the engineering effort and propensity for errors.This paper introduces a model-based approach to analyzing and adapting the interface of aMFM automatically in order to enable interaction with neighboring modules across different version numbers, i.e. modules containing a different version number. Hence, the engineering effort and error-proneness for the adaption can be reduced. For instance, the automatic analysis and adaption of the interface of a newly-developed QR Code Scanner is considered here.

Soft simulator for redesigning of a chickpea harvester header

This paper proposes a new framework to tackle computer-aided modeling and evolutionary algorithms into conventional design phase of a machine, which, in turn, significantly reduces time and cost of structural optimization. The model-based engineering approach overcomes the crudity of hard modeling, field experiments and statistical analysis for finding the optimum structure of a design. Genetic algorithm incorporated with fuzzy modeling established a hybrid computational algorithm which predicts optimal sizing of a platform, developed for a chickpea harvester header. The harvesting losses of the platform’s configurations in field trials were fed to the metaheuristic approach to develop a soft simulator for redesigning of the machine. Acceptable harvesting performance of the optimized harvester in field trials confirmed the robustness feature of the experiments based simulator. Further the results validated the virtual model and verified the reliability of the automatically generated harvester. The methodology can be employed for structural optimization of mechanical systems.

Enhancing a model-based engineering approach for distributed manufacturing automation systems with characteristics and design patterns

Recent trends in modern manufacturing, such as the growing need for flexibility and the increasing degree of automation in industrial facilities, require distributed control solutions. Implementations of such control schemas and underlying architectures come along with an exponential increase of the automation system's complexity. Therefore, methods for supporting automation engineers during the development processes are highly required. This paper presents an approach to supporting model-based engineering (MBE) of distributed manufacturing automation systems. The approach is based on the combination of notation, characteristics, and design patterns across multiple levels of an adapted development process. Accordingly, a prototypical support tool has been implemented. The modeling approach has been evaluated by case studies and additional usability experiments to determine the benefit of its application within the design of manufacturing automation systems.

Model-based system configuration approach for Internetware

Internetware, a new software paradigm, enables systems to be built and evolved for better service quality according to changes in the environment. However, at the same time Internetware greatly complicates system management tasks due to the autonomy, cooperation and evolution of its components. In fact, the configuration of large-scale Internet-based software in the real scenarios is likely to involve a large number of components and hundreds of associated configuration options with variables, whose values may be dynamically determined depending on the execution environment, or to cope with needs of customers. In this paper, we firstly analyze the configuration issues of Internetware, and then present a model-based engineering approach to managing the configuration for Internetware systematically. Based on the Architecture Based Composition model, an internetware configuration process model is proposed that abstracts the configuration in the lifecycle of Internetware. Focusing on the fundamental activities in configuration process, the methods and mechanisms of analyzing and performing the configuration in terms of its constraints, dependencies, heterogeneity and dynamics, with supporting tools to realize the configuration automation, are presented. The proposed methods and mechanisms have been implemented, validated and rolled out in IBM software products and Cloud center.

Transformation of Simulink models to IEC 61499 Function Blocks for verification of distributed control systems

In this paper, a new model-based engineering approach is introduced by bridging MATLAB Simulink with IEC61499 Function Block models. This is achieved by a transformation between the two block-diagram languages. The transformation supported by the developed tools sets the cornerstone of the verification and validation framework for IEC 61499 Function Blocks in closed-loop with the models of the plant. The framework also paves the way to running distributed simulations of complex hybrid (i.e., continuous-discrete) closed-loop plant-controller systems and building complex models using the efficient object instantiation techniques of IEC 61499.

Research directions in energy-sustainable cyber–physical systems

An overview of sustainable computing is provided and different approaches towards design and verification of energy-sustainable computing (i.e., sustainable computing from energy consumption perspective) are discussed for cyber–physical systems (CPSs), i.e., systems with strong coupling between computing components and non-computing processes in physical environment. A major issue in this regard is the inter-dependencies of the non-computing processes on the computing components and vice versa, and the verification of the CPSs’ sustainability without real deployment. The trends and dependencies of energy consumption for both computing and non-computing components are conceptualized. Based on this conceptualization, CPS resource management algorithms are categorized according to: (i) computing workload execution and arrival profiles supported, (ii) knowledge of workload profiles during management decision making, (iii) support of power management in the computing components, and (iv) assumptions on non-computing process behavior. These categories are then discussed along with their pros and cons for two representative CPSs: data centers and body sensor networks (BSNs). A model based engineering approach is used to verify CPS sustainability before real deployment. Several research directions and open problems are further discussed for the design and verification of energy-sustainable CPSs.

Analysis of a Complex System for Electrical Mobility Using a Model-Based Engineering Approach Focusing on Simulation

Electrical mobility is a highly innovative field with fast development cycles and rapid evolution of its core architectures. Also, the system complexity is quickly increasing. Especially challenging in industrial settings is the tight integration of mechanical, electrical and software domains within so called mechatronic systems. In modern engineering processes, simulation models are used for the analysis of different aspects of such systems.In this paper, we report our experience with a model-based and simulation-focused methodology for system engineering which allows quick validation of different architectural decisions at early development phases. We present an approach for integration between engineering tools using the automated generation of simulation models from the system description. The feasibility of the proposed methods is demonstrated with a sample application from electrical mobility, which also exemplifies the challenges of the integrated analysis of mechatronic systems. Specialized modeling languages based on the UML standard are used for the overall description of the system, for the analysis of its functionality, and for the specification of combined configurations. Another standard, Modelica language, is used for the simulation of different physical aspects of the system.

Model-based evolution of collaborative agent-based systems

Abstract As demands for behaviorally sophisticated software grow, agent-based systems are increasingly being employed. Software agents are frequently applied to large, complex systems that involve interdisciplinary development teams. These complex systems have proved to be challenging to develop and evolve, even for the most competent software engineers. Taking lessons learned in other engineering disciplines such as computer and architectural engineering we investigated a model-based engineering approach called Model-Driven Architecture (MDA) to automate, whenever possible, the development and evolution of agent-based applications. In our investigation, we use the Cognitive Agent Architecture (Cougaar); one of the most mature and sophisticated collaborative agent-based architectures. MDA and Cougaar served as the primary components and implementation platform for our research. In this paper we present our approach and demonstrate how MDA is effective for producing sophisticated agent-based systems. A key challenge was found in designing a flexible meta-model framework that would accommodate both top-down domain information and bottom-up platform specific constructs, as well as the transformations and mappings between them. We employed a General Domain Application Model (GDAM) as the platform-independent model layer and General Cougaar Application Model (GCAM) layer as the platform specific model respectively. Domain-level requirements are formulated using a XML Process Definition Language (XPDL) based graphical editor and are the refined through a series of model transformations (via the underlying metamodel) to systematically generate the agent-based software system. Through an illustrative case-study, we report on the feasibility, strengths and limitations of the model-based approach as it was investigated with the Cougaar.

Model-based engineering for change-tolerant systems

Developing and evolving today’s systems are often stymied by the sheer size and complexity of the capabilities being developed and integrated. At one end of the spectrum, we have sophisticated agent-based software with hundreds of thousands of collaborating nodes. These require modeling abstractions relevant to their complex workflow tasks as well as predictable transforms and mappings for the requisite elaborations and refinements that must be accomplished in composing these systems. At the other end of the spectrum, we have ever-increasing capabilities of reconfigurable hardware devices such as field-programmable gate arrays to support the emerging adaptability and flexibility needs of these systems. From a model-based engineering perspective, these challenges are very similar; both must move their abstraction and reuse levels up to meet growing productivity and quality objectives. Model-based engineering and software system variants such as the model-driven architecture (MDA) are increasingly being applied to systems development as the engineering community recognizes the benefits of managing complexity, separating key concerns, and automating transformations from high-level abstract requirements down through the implementation. However, there are challenges when it comes to establishing the correct boundaries for change-tolerant parts of the system. Capabilities engineering (CE) is a promising approach for defining long-lived components of a system to ensure some sense of change tolerance. For innovative initiatives such as the National Aeronautics and Space Administration (NASA)’s autonomous nanotechology swarms (ANTS), the development and subsequent evolution of such systems are of considerable importance as their missions involve complex, collaborative behaviors across distributed, reconfigurable satellites. In this paper, we investigate the intersection of these two technologies as they support the development of complex, change-tolerant systems. We present an effective approach for bounding computationally independent models so that, as they transition to the architecture, capabilities-based groupings of components are relevant to the change-tolerant properties that must convey in the design solution space. The model-based engineering approach is validated via a fully functional prototype and verified by generating nontrivial multiagent systems and reusing components in subsequent systems. We build off of this research completed on the collaborative agent architecture, discuss the CE approach for the transition to architecture, and then examine how this will be applied in the reconfigurable computing community with the new National Science Foundation Center for High-Performance Reconfigurable Computing. Based on this work and extrapolating from similar efforts, the model-based approach shows promise to reduce the complexities of software evolution and increase productivity—particularly as the model libraries are populated with canonical components.

Toward an intelligent machine tool for flexible manufacturing

The need to manufacture accurate and inexpensive components in small batches has spurred the development of flexible manufacturing systems (FMSs). This paper reviews the basic components of FMSs and describes efforts currently underway at Carnegie-Mellon University to create an autonomous machine tool for the factory of the future. The machine tool would operate under computer control for part handling (manipulation), part fixturing, and metal cutting. This work argues for model-based engineering approaches integrated with expert systems that attempt to encapsulate the craftsmanships skills of human expert machinists.