Model-based Development Process Research Articles

Case Study of Power Supply Products Applying Model-Based Development Process

Case Study of Power Supply Products Applying Model-Based Development Process

Finding Locally Smallest Cut Sets using Max-SMT

Model-based development (MBD) is increasingly being used for system-level development of safety-critical systems. This approach allows safety engineers to leverage the system model created during the MBD process to assess the system's resilience to component failure. In particular, one fundamental activity is the identification of minimal cut sets (MCSs), i.e, minimal sets of faults that lead to the violation of a safety requirement. Although the construction of a formal system model enables safety engineers to automate the generation of MCSs, this is usually a computationally expensive task for complex enough systems. We present a method that leverages Max-SMT solvers to efficiently obtain a small set of faults based on a local optimization of the cut set cardinality. Initial experimental results show the effectiveness of the method in generating cut sets that are close or equal to globally optimal solutions (smallest cut sets) while providing an answer 5.6 times faster on average than the standard method to find a smallest cut set.

Randomized reachability analysis in UPPAAL: fast error detection in timed systems

Randomized reachability analysis is an efficient method for detection of safety violations. Due to the under-approximate nature of the method, it excels at quick falsification of models and can greatly improve the model-based development process: using lightweight randomized methods early in the development for the discovery of bugs, followed by expensive symbolic verification only at the very end. We show the scalability of our method on a number of timed automata and stopwatch automata models of varying sizes and origin. Among them, we revisit the schedulability problem from the Herschel–Planck industrial case study, where our new method finds the deadline violation three orders of magnitude faster: some cases could previously be analyzed by statistical model checking in 23 h and can now be checked in 23 s. Moreover, a deadline violation is discovered in a number of cases that were previously intractable. We have implemented the Randomized reachability analysis—and made it available—in the tool Uppaal. Finally, we provide an evaluation of the strengths and weaknesses of Random reachability analysis exploring exactly which types of model features hamper method’s efficiency.

DESIGNING A MODEL-BASED, MULTI-PERSPECTIVE PROCESS DESIGN ENVIRONMENT

AbstractThe development of engineered systems is a complex process, involving many activities and development artifacts. The challenges introduced by this complexity calls for a model-based design environment. The PROVE methodology is an artifact-centric, model-based development process design methodology. In this paper, we present the first, fully model-based design environment of the PROVE. Furthermore, our environment incorporates additional process design perspectives that are applied in real-time and in a completely consistent manner, based on a formally defined information model. We discuss the details of our environment's design as well as highlight its potential benefits.

Recommender systems in model-driven engineering

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

The functionality advancements and novel customer features that are currently found in modern automotive systems require high-bandwidth and low-latency in-vehicle communications, which become even more compelling for autonomous vehicles. In a recent effort to meet these requirements, the IEEE Time-Sensitive Networking (TSN) task group has developed a set of standards that introduce novel features in Switched Ethernet. TSN standards offer, for example, a common notion of time through accurate and reliable clock synchronization, delay bounds for real-time traffic, time-driven transmissions, improved reliability, and much more. In order to fully utilize the potential of these novel protocols in the automotive domain, TSN should be seamlessly integrated into the state-of-the-art and state-of-practice model-based development processes for automotive embedded systems. Some of the core phases in these processes include software architecture modeling, timing predictability verification, simulation, and hardware realization and deployment. Moreover, throughout the development of automotive embedded systems, the safety and security requirements specified on these systems need to be duly taken into account. In this context, this work provides an overview of TSN in automotive applications and discusses the recent technological developments relevant to the adoption of TSN in automotive embedded systems. The work also points at the open challenges and future research directions.

Requirements for Model-Based Development Process Design and Compliance of Standardized Models

The planning of system development efforts is crucial to the successful realization of projects. However, development planning typically lacks systematic, engineering discipline, and consequently risks project and business success. Model-based process design is a potential information systems approach to addressing the increasing complexity of such planning. We characterize the ontology of development process design, based on real-life observations and scientific publications. We then synthesize the required ontology with the desirable characteristics of models, and derive key requirements for model-based development process design. Next, these requirements are used to evaluate the adequacy of three prominent, standardized model-based process design approaches—BPMN, OPM and SPEM. The findings reveal that the surveyed models are a partial fit, and do not promote sound process design. Finally, by generalizing the categorical evaluation results, possible root causes for the identified inadequacies are proposed. A new model design, which should rely on the formulated requirements set, is called for, in pursuit of a wider adoption of model-based design paradigms and better information systems realization to support the development of complex systems.

Robustness of AI-based prognostic and systems health management

Prognostic and systems Health Management (PHM) is an integral part of a system. It is used for solving reliability problems that often manifest due to complexities in design, manufacturing, operating environment and system maintenance. For safety-critical applications, using a model-based development process for complex systems might not always be ideal but it is equally important to establish the robustness of the solution. The information revolution has allowed data-driven methods to diffuse within this field to construct the requisite process (or system models) to cope with the so-called big data phenomenon. This is supported by large datasets that help machine-learning models achieve impressive accuracy. AI technologies are now being integrated into many PHM related applications including aerospace, automotive, medical robots and even autonomous weapon systems. However, with such rapid growth in complexity and connectivity, a systems’ behaviour is influenced in unforeseen ways by cyberattacks, human errors, working with incorrect or incomplete models and even adversarial phenomena. Many of these models depend on the training data and how well the data represents the test data. These issues require fine-tuning and even retraining the models when there is even a small change in operating conditions or equipment. Yet, there is still ambiguity associated with their implementation, even if the learning algorithms classify accordingly. Uncertainties can lie in any part of the AI-based PHM model, including in the requirements, assumptions, or even in the data used for training and validation. These factors lead to sub-optimal solutions with an open interpretation as to why the requirements have not been met. This warrants the need for achieving a level of robustness in the implemented PHM, which is a challenging task in a machine learning solution.This article aims to present a framework for testing the robustness of AI-based PHM. It reviews some key milestones achieved in the AI research community to deal with three particular issues relevant for AI-based PHM in safety-critical applications: robustness to model errors, robustness to unknown phenomena and empirical evaluation of robustness during deployment. To deal with model errors, many techniques from probabilistic inference and robust optimisation are often used to provide some robustness guarantee metric. In the case of unknown phenomena, techniques include anomaly detection methods, using causal models, the construction of ensembles and reinforcement learning. It elicits from the authors’ work on fault diagnostics and robust optimisation via machine learning techniques to offer guidelines to the PHM research community. Finally, challenges and future directions are also examined; on how to better cope with any uncertainties as they appear during the operating life of an asset.

Design and Practice of a Model-Based Development Education in Hydraulic Systems

This paper proposes educational simulation environment for the model-based development (MBD) of hydraulic systems. In recent years, customer requirements for different products have become more diversified and increasingly complicated. Thus, companies must respond to such demands promptly. Many companies are beginning to adopt an MBD as a solution to this problem because such an approach can reduce the development time and cost. However, most engineers in such companies lack experience with an MBD. Therefore, establishing a training environment that can allow them to learn the MBD process experientially within a short timeframe is an effective approach. Moreover, because hydraulic systems are mainly used in construction machines, this study focuses on educational materials for hydraulic systems through an MBD educational program jointly developed between Hiroshima University and Kobelco Construction Machinery Co., Ltd.

Improving collapsibility robustness of an EPS-CD by means of simulation and six sigma

Steering columns for column assist Electric Steering are one of the main elements of the passive safety restraint system in a passenger vehicle. It is designed to collapse applying an optimized ride down force to reduce chest and head injuries of the driver during a frontal crash event, making this requirement one of the most important ones for its development. The system crash response is generated by several elements and interfaces due to the fact of many other different product requirements that a steering column must fulfill. This could lead into a complex validation process throughout the development, as it is needed to integrate the product into the vehicle in order to meet the passive safety requirements. As part of ZF lean Validation approach a new method has been developed to improve the system robustness in ZF Active safety division by means of virtual simulation and six sigma disciplines. This resultant method is also applicable for a model based development process and also contributes to improve frontloading development techniques. An analysis of variance has been done using a correlated virtual model for crash simulation, where the main parameters of the system are known (normally the case when same product generation is reused in a new vehicle). Obtaining as a result of the analysis the regression equation of the system response. This equation defines and quantifies the importance of each factor, and consequently is defining the component contribution and how to develop a column from bottom to top. Moreover, as a second step, a Monte Carlo analysis has been applied using the equation obtained generating statistical results for a number of cases (based in the design tolerances) not manageable from the hardware testing perspective neither from the finite elements methods. In that project Monte Carlo analysis has been applied for 100,000 cases generating response confident intervals instead of the classic FEM deterministic response where only the nominal case is considered.

Validation of decision logic of an autoland system for a UAV using model-based safety-assessment techniques

Software of automatic flight control systems requires thorough verification and validation. Traditionally, this is achieved with elaborate development processes following pertinent industry standards. To reduce the development effort, however, new methods have emerged: a model-based software development process is used at the Institute of Flight System Dynamics of the Technical University of Munich for the design of auto-flight systems with MATLAB/Simulink. Besides, the model-based safety assessment (MBSA) framework ExCuSe has been developed, which implements methods for fault modeling and automatic cut-set extraction using the Simulink Design Verifier. This paper proposes an application of MBSA techniques for the efficient requirements and design validation of decision logic in auto-flight-system software. With ExCuSe, software design models of an investigated decision logic are supplemented by models for off-nominal inputs (e.g., a sensor fault) and for the design requirements. With the analysis, either a formal proof is obtained that the investigated decision logic fulfills the requirements under any circumstances (guaranteed properties), or a counterexample illustrates a requirement violation. The functional principle and applicability of the method are demonstrated by the analysis of decision logic of the autoland system of the SAGITTA Demonstrator UAV. ExCuSe is used to prove that the logic guarantees a timely flare initiation so that a safe touchdown sink rate is achieved despite altitude-measurement inaccuracy and closed-loop flare dynamics uncertainty. As virtually all auto-flight systems feature decision logic, this initial demonstration of the technique opens up many opportunities for further applications in future work.

A formal approach to AADL model-based software engineering

Formal methods have become a recommended practice in safety-critical software engineering. To be formally verified, a system should be specified with a specific formalism such as Petri nets, automata and process algebras, which requires a formal expertise and may become complex especially with large systems. In this paper, we report our experience in the formal verification of safety-critical real-time systems. We propose a formal mapping for a real-time task model using the LNT language, and we describe how it is used for the integration of a formal verification phase in an AADL model-based development process. We focus on real-time systems with event-driven tasks, asynchronous communication and preemptive fixed-priority scheduling. We provide a complete tool-chain for the automatic model transformation and formal verification of AADL models. Experimentation illustrates our results with the Flight control system and Line follower robot case studies.

Influence awareness: considering motivation in computer-assisted rehabilitation

The use of computers in the e-Health domain is becoming increasingly common, since technology is present in most aspects of our lives. In the rehabilitation field in particular, some additional issues requiring the use of computer-assisted therapies arise. On the one hand, there is a scarce availability of rehabilitation specialists and centers to satisfy the growing demand of their services. This problem gets even magnified because of the ageing population. On the other hand, the huge opportunities that the new interaction devices can bring to rehabilitation smooth the path towards novel therapies. Nevertheless, even if a proper rehabilitation therapy is prescribed, it can fail because of the patient´s lack of motivation There are assorted motivation theories available in the literature to address this demotivation of patients. Unfortunately, there is no model or guide to put those theories into practice in computer-assisted rehabilitation. This paper is aimed at filling this gap by providing a model, namely Influence Awareness, to support the specification of motivation aspects in those applications used in computer-assisted rehabilitation. Furthermore, some guidelines are also provided, so that the designer can get some extra guidance on some heuristics about how to design motivation. The integration of motivation design into a model-based development process is presented by showing how this motivation model is integrated into a task model. Finally, to better illustrate our approach a case study based on a collaborative e-Health system is also included.

Multidimensional context modeling applied to non-functional analysis of software

Context awareness is a first-class attribute of today software systems. Indeed, many applications need to be aware of their context in order to adapt their structure and behavior for offering the best quality of service even in case the software and hardware resources are limited. Modeling the context, its evolution, and its influence on the services provided by (possibly resource constrained) applications are becoming primary activities throughout the whole software life cycle, although it is still difficult to capture the multidimensional nature of context. We propose a framework for modeling and reasoning on the context and its evolution along multiple dimensions. Our approach enables (1) the representation of dependencies among heterogeneous context attributes through a formally defined semantics for attribute composition and (2) the stochastic analysis of context evolution. As a result, context can be part of a model-based software development process, and multidimensional context analysis can be used for different purposes, such as non-functional analysis. We demonstrate how certain types of analysis, not feasible with context-agnostic approaches, are enabled in our framework by explicitly representing the interplay between context evolution and non-functional attributes. Such analyses allow the identification of critical aspects or design errors that may not emerge without jointly taking into account multiple context attributes. The framework is shown at work on a case study in the eHealth domain.

Model-based automatic test case generation for automotive embedded software testing

We propose a method to automatically generate software and hardware test cases from a UML model developed through a model-based development process. Where languages such as source-code languages are used within the model, input and expected values for each test case are generated using a custom parser. As a next step, unit test cases are combined to generate integration test cases using a bottom-up approach. Then these cases are converted into hardware test cases for approval testing of embedded systems, using XQuery and hardware mapping tables. We demonstrate this process by applying it to the power window switch module of a Hyundai Santa Fe vehicle. Our approach provides an automatic testing procedure for embedded systems developed by model-based methods, and generates test cases efficiently using a recombination of signals. In conclusion, our proposed method could help reduce the resources needed for test case generation from software to hardware.

Optimized Design of Thermoelectric Energy Harvesting Systems for Waste Heat Recovery from Exhaust Pipes

With the increasing interest in energy efficiency and resource protection, waste heat recovery processes have gained importance. Thereby, one possibility is the conversion of the heat energy into electrical energy by thermoelectric generators. Here, a thermoelectric energy harvesting system is developed to convert the waste heat from exhaust pipes, which are very often used to transport the heat, e.g., in automobiles, in industrial facilities or in heating systems. That is why a mockup of a heating is built-up, and the developed energy harvesting system is attached. To build-up this system, a model-based development process is used. The setup of the developed energy harvesting system is very flexible to test different variants and an optimized system can be found in order to increase the energy yield for concrete application examples. A corresponding simulation model is also presented, based on previously developed libraries in Modelica®/Dymola®. In the end, it can be shown—with measurement and simulation results—that a thermoelectric energy harvesting system on the exhaust pipe of a heating system delivers extra energy and thus delivers a contribution for a more efficient usage of the inserted primary energy carrier.

A framework for relating syntactic and semantic model differences

Model differencing is an important activity in model-based development processes. Differences need to be detected, analyzed, and understood to evolve systems and explore alternatives. Two distinct approaches have been studied in the literature: syntactic differencing, which compares the concrete or abstract syntax of models, and semantic differencing, which compares models in terms of their meaning. Syntactic differencing identifies change operations that transform the syntactical representation of one model to the syntactical representation of the other. However, it does not explain their impact on the meaning of the model. Semantic model differencing is independent of syntactic changes and presents differences as elements in the semantics of one model but not the other. However, it does not reveal the syntactic changes causing these semantic differences. We define Diffuse, a language-independent, abstract framework, which relates syntactic change operations and semantic difference witnesses. We formalize fundamental relations of necessary, exhibiting, and sufficient sets of change operations and analyze their properties. We further demonstrate concrete instances of the Diffuse framework for three different popular modeling languages, namely class diagrams, activity diagrams, and feature models. The Diffuse framework provides a novel foundation for combining syntactic and semantic differencing.

Process Planning and Decentralised Process Control for Cybertronic Production Systems

Cybertronic systems which augment mechatronic basis systems by means of communication, collaboration and intelligence are proposed to be utilised to develop next generation production systems. A model-based development process is proposed to enable integrated design and development of the product and the production system. This paper will highlight the importance of process planning as collaborative activity within the integrated design and development process. A concept will be introduced how process planning can be conducted for cybertronic production systems, considering decentralised production planning and control already during the design phase.

Multiphysics Simulation in the Development of Thermoelectric Energy Harvesting Systems

This contribution presents a model-based development process for thermoelectric energy harvesting systems. Such systems convert thermal energy into electrical energy and produce enough energy to supply low-power devices. Realizations require three main challenges to be solved: to guarantee optimal thermal connection of the thermoelectric generators, to find a good design for the energy harvesting system, and to find an optimal electrical connection. Therefore, a development process is presented here. The process is divided into different steps and supports the developer in finding an optimal thermoelectric energy harvesting system for a given heat source and given objectives (technical and economical). During the process, several steps are supported by simulation models. Based on developed model libraries in Modelica®/Dymola®, thermal, thermoelectrical, electrical, and control components can be modeled, integrated into different variants, and verified step by step before the system is physically built and finally validated. The process is illustrated by an example through all the steps.

Model-Based Development Process of Cybertronic Products and Production Systems

The shift to satisfied customer markets forces manufacturers to offer customised products. Moreover, product lifecycles are shortened, which requires a faster development of products and corresponding production systems. Both challenges amplify complexity in production. This complexity is usually confronted with flexibility. A new approach offering decentralised structures, and thereby flexibility, comes from cybertronic systems (CTS), which are further developed mechatronic systems with the capability to communicate through open networks with other such mechatronic systems. Up to now no integrated development process to engineer cybertronic products (CTP) and production systems (CTPS) has been developed, although such a process is essential to use their beneficial properties for today’s market conditions. Therefore, research is conducted in the research project mecPro². First, the properties of cybertronic systems are investigated and dissociated from those of mechatronic systems. Based on these properties, the connections of CTP and CTPS are analysed and a systematics for description for both is identified. With this the model-based development processes of CTP and CTPS can be further defined as well as their intersections and afterwards implemented in a data model. Finally, the development process is summarised in a product lifecycle management software to support the development process.