Verification Of System Models Research Articles

Empowering Model‐Based Systems Engineering Through Metamodeling

AbstractA critical enabler for Model‐Based Systems Engineering (MBSE) and Digital Engineering (DE) is the generation of coherent and consistent views of a system‐of‐interest based on information within a system model. In practice, system model development is facilitated through domain‐specific profiles, style guides, reference models, and low‐fidelity meta‐models to create coherent and consistent system information. Each of these approaches are useful but are insufficient for robust and automated verification of system models to an ideal. Furthermore, the expression of domain‐specific concepts and semantics relies on the proliferation of non‐standard, domain‐specific profiles as standard system modeling languages like the Systems Modeling Language (SysML) are general purpose. This paper proposes a novel approach to creating precise, machine‐interpretable metamodels implemented as a lightweight Unified Modeling Language (UML) profile. The profile includes numerous features that allow model architects to quickly specify context and domain‐specific modeling constructs without creating non‐standard stereotypes to apply domain‐specific meaning and usage rules. Three kinds of constraints can be inferred based on the relationships between meta‐model elements: type, multiplicity, and default value. Applications of well‐formed metamodels include a shared understanding of the intended model format and structure, as well as the one‐time programmatic generation of an encompassing suite of validation rules to evaluate a system model against the inferred constraints, thus ensuring consistency. Additional applications include programmatic generation of model analysis metrics, system models from metamodels, metamodels from reference models and element finding queries, and the ability to update a system model based upon the updated metamodel automatically. Use of the approach results in reduced time in system model development and analysis and ensures coherency and consistency of information thus increasing stakeholder use and confidence in the system model.

Formally proving whether the software implementation of models applied in instrumentation and control systems conform to the specified requirements

Research objective is to study the possibility to formally validate whether the model’s software implementation meets all the specified requirements of the systems, the model of which can be represented in the form of finite-state automata. Research relevance. At one of the first stages, the development of software for instrumentation and control systems provides for the creation of the system model. The model is based on the terms of reference, specification, and various a priori information. Most of the models for engineering systems in the modern mining industry (conveyor systems, ventilation systems, etc.) can be described in terms of the finite state automaton model. Such a model can be applied to solve diverse tasks. The next step is to implement the model in whole or in part. In this context, the task arises to determine the model’s software implementation conformity to its initial description. Results. One way to solve the task is to formally prove that the software model possesses the properties which are provided in the specification (description) of the initial model. By the example of the mine conveyor system, the paper illustrates the application of the method which consists in the software implementation of the corresponding finite-state automaton model, forecasting whether the model possesses the properties through theorems and their subsequent proof by applying special software. Conclusions. Formal methods of specification, development, and verification of system models’ software implementation together with other methods make it possible to improve the quality and reliability of solutions under development.

Constructing Models for Systems Resilience: Challenges, Concepts, and Formal Methods

As systems continue to grow in scale and complexity and have to operate safely in challenging disruptive environments, system safety and resilience has become a critical requirement. This recognition has drawn attention to the concept of resilience, which has different definitions and several different interpretations that tend to be domain specific. For example, resilience in health care clinics means something quite different than resilience in self-driving cars, or energy grids. This paper reviews the different characterizations of resilience and assesses their value proposition in realizing engineered resilient systems. This paper emphasizes the importance of systems modeling in engineering resilient systems and presents an overarching methodology that employs different modeling approaches for operational tasks as a function of problem context. This paper specifically focuses on systems modeling in partially observable and potentially hostile environments. It discusses the need for system model verification, which is key to safety, and system flexibility and adaptability, which are key to resilience. It introduces a formal, probabilistic modeling construct called the “resilience contract.” This construct employs a state-based representation that formalizes the concept of resilience while enabling system model verification and affording requisite flexibility for adaptation and learning. The key findings of our research are that different system modeling approaches and algorithms are needed based on mission tasks and operational context; adaptive capacity and continual adaptability are the two promising characterizations of resilience that can be cost-effectively realized in real-world systems; and the resilience contract construct is an effective means for probabilistic verification of system model correctness while affording flexibility needed for adaptation and learning. Collectively, these findings contribute to the body of knowledge in both model-based systems engineering (MBSE) and engineered resilient systems.

Test Generation and Test Prioritization for Simulink Models with Dynamic Behavior

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Among the different disciplines, the engineering of Cyber Physical Systems (CPSs) particularly relies on models with dynamic behaviors (i.e., models that exhibit time-varying changes). The Simulink modeling platform greatly appeals to CPS engineers since it captures dynamic behavior models. It further provides seamless support for two indispensable engineering activities: (1) automated verification of abstract system models via model simulation, and (2) automated generation of system implementation via code generation. We identify three main challenges in the verification and testing of Simulink models with dynamic behavior, namely incompatibility, oracle and scalability challenges. We propose a Simulink testing approach that attempts to address these challenges. Specifically, we propose a black-box test generation approach, implemented based on meta-heuristic search, that aims to maximize diversity in test output signals generated by Simulink models. We argue that in the CPS domain test oracles are likely to be manual and therefore the main cost driver of testing. In order to lower the cost of manual test oracles, we propose a test prioritization algorithm to automatically rank test cases generated by our test generation algorithm according to their likelihood to reveal a fault. Engineers can then select, according to their test budget, a subset of the most highly ranked test cases. To demonstrate scalability, we evaluate our testing approach using industrial Simulink models. Our evaluation shows that our test generation and test prioritization approaches outperform baseline techniques that rely on random testing and structural coverage.

Formalization of UML Composite Structure using Colored Petri Nets

Design specification and requirement analysis, during development process involved in transformation of real world problems to software system are subjected to severe issues owing to involvement of semantics. Though, for design and specification of object-oriented systems, Unified Modeling Language (UML) is now recognized as standard language however, its structures have numerous drawbacks which include lack of semantics definition and unidentified deadlocks. The research work proposes a model to avoid deadlocks, specifically in composite structure of UML. Verification of system models by formal methods holds significance, particularly, at requirement specification and design level, to ensure the accuracy of models and high light the design problems before implementation. The paper proposes the rules that allow software engineers to formalize the behavior of UML 2.0 composite structure using Colored petri nets. Using these rules, the research shall analyze the correspondent Colored petri nets and conclude the properties of the original work flow, using theoretical outcomes in the Colored petri nets domain.

Simulating simulation-agnostic SysML models for enterprise information systems via DEVS

Systems Modeling Language (SysML) is used as the modeling infrastructure in systems engineering, especially for complex systems design, independently of the system domain. Simulation is a common method to perform system model verification, during the systems development process. However, simulation code generation and execution is not integrated within the system design activity, as it is facilitated by SysML. It is either conducted as an external activity, after system design, or it affects the system design environment and practices, according to specific simulators requirements.This paper presents how existing, simulation-agnostic SysML models from the domain of Enterprise Information System (EISs), can be transformed to executable simulation code and in addition how the simulation results can be incorporated into the source SysML model through the exploitation of Model Driven Architecture (MDA) principles and techniques. To this end, several tools and technologies are utilized, while the verification process is triggered and finalized via the system modeling environment. Adoption of MDA provides a solid, high-level infrastructure and tool availability to the proposed approach.

On the Importance of Power System Model Verification

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Identification of excitation system transfer functions in hydro power plants using a binarypseudo random signal

In several countries, field tests for excitation system model verification have become mandatory. Therefore on-site transfer function identification of hydro units are compulsory. Using excitation system implemented Pseudo-Random Binary Sequence signal instead of a solid state spectrum analyser is a commercial solution. A correct transfer function of a generating unit is of high importance for modelling the power system, since it helps to understand behaviour, predict failures and improve stability and dynamic performance. Especially on hydro units these tests can verify tuning quality of excitation systems and power system stabilisers.

FORMAL CONCEPT ANALYSIS FOR BUSINESS INFORMATION SYSTEMS

In this paper, we present a methodology based on formal concept analysis (FCA) for the information system model verification. We show that FCA can be useful for understanding conceptual model topology and it can be used to improve the structure of the conceptual model. IBM's Information Framework (IFW) Financial Services Data Model (FSDM) has been used for the present research. By using FSDM, we demonstrate that the IS model can be recreated with formal concept analysis.

NERC power industry policies

This article describes the performance testing of the generator excitation system which is likely to be mandatory. Today, testing requirements or specific feature requirements such as a power system stabiliser, may vary depending upon region, but testing and verification of system models will be important tools to ensure future reliability of the transmission system. The requirements for generator testing as stated in NERC planning standard II.B is discussed. This article also discusses the various test and reasons for operating in suggested characteristic mode.

Modelling concepts arising from an investigation into a chaotic system

The Chua circuit, a simple third-order nonlinear dynamical system that exhibits chaotic behaviour, has highlighted shortcomings in presently used analysis and design techniques. We discuss the way in which chaotic dynamical behaviour in systems has affected many current ideas about the randomness of noise, the use of simulation, reducing problems to subproblems, the value of experiments, the use of nonlinear controllers, and the verification of system models.