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Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain., QC 20211108

Model-based Systems Engineering (MBSE) is a noval approach to support complex system development by formalizing system artifacts and development using models. Though MBSE models provide a completely structural formalisms about system development for system developers, such large of domain specific knowledge represented by models cannot be captured as what the developers expect. This leads to a big challenge when MBSE can be widely used for complex system development. In this paper, a knowledge management approach is proposed to support an intelligent question answering scenario when implementing MBSE in system lifecycle. We make use of the GOPPRR approach to support MBSE formalisms which are transformed to knowledge graph models. Then such models provide cues for intelligent question answers through reasoning. In the case study, we make use of an auto-braking system scenario to develop MBSE models and to implement the intelligent question answering. Finally, we find the availability of our approach is evaluated which the domain engineers enable to capture their domain knowledge more efficiently., Part of proceedings: ISBN 978-3-030-72650-8QC 20220316

Model-based systems engineering (MBSE) has been accepted as an extremely important approach to understanding the multi-domain research field comprehensively. In this paper, a bibliometric analysis is used to conduct a comprehensive survey of model-based systems engineering literature of the last five years. The VOSViewer is used to implement visual network analysis for co-authorship, citation, and co-occurrence of the MBSE related papers. The results of the bibliometric study, firstly, reveal the influential research teams and sources. Secondly, research hotspots in the current MBSE domain are identified. The findings of this study aim to help researchers to gain a faster and deeper understanding of the current literature on MBSE., QC 20221005part of proceedings ISBN 9781665431682

Model-based systems engineering (MBSE) enables to verify the system performance using system behavior models, which can identify design faults that do not meet the stakeholders' requirements as early as possible, thus reducing the R&D cost and error risks. Currently, different domain engineers make use of different modeling languages to create their own behavior models. Different behavior models are verified by different approaches. It is difficult to adopt a unified integrated platform to support the modeling and verification of heterogeneous behavior models during the conceptual design phase. This paper proposes a unified modeling and verification approach supporting system formalisms and verification. The KARMA language is used to support the unified formalisms across MBSE models and dynamic simulations for different domain specific models. In order to describe the behavior model more precisely and to facilitate verification, the syntax of hybrid automata is integrated into KARMA. We implemented behavior models and their verification in MetaGraph, a multi-architecture modeling tool. Finally, the effectiveness of the proposed approach is validated by two cases: 1) the scenario of booking railway tickets using BPMN models; 2) the behavior performance simulation of unmanned vehicles using a SysML state machine diagram., QC 20220602part of proceedings ISBN 9781450391061

The latest developments in Model-Based Systems Engineering (MBSE) and Product Life-Cycle Management (PLM) are playing a role in the evolution of the aeronautical industry. Despite the reluctance of this domain to accept the introduction of technology leaps in the production process - mostly due to safety reasons - aircraft manufacturers are slowly moving to a new digital factory concept. The deployment of a PLM Tool for Aircraft Ground Functional testing with Eco-design criteria can be leveraged to improve both sustainability of the assembly line and efficiency of the Ground System Tests process end to end, however, heterogeneous data interoperability represents one of the major challenges in this framework. The ontology-based solution proposed in this work addresses this challenge, thus, shows how semantics can be exploited to stream line the data pipeline throughout a PLM digital platform.

We analyze the absorptive capacity (AC) process of a manufacturing company with central R&D and an internationally distributed manufacturing network. Prior research shows that an implementation of the lead factory (LF) is especially supportive if the international manufacturing network struggles with implementing new products and processes. We analyze determinants of AC and show that, in addition to prior related knowledge of the receiving plant, structure can have an even stronger influence. We show that in the case of a low level of prior related knowledge and a low level of AC within the receiving plants as well as high technological heterogeneity between plants and LF, the implementation of an LF may not lead to the expected result. In addition, we conclude that the analysis of the AC process has to move from a single unit to a network. This helps to understand the AC concept in the context of multinational companies.

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.