Runtime observable and adaptable UML state machines: [email protected] approach

n embedded system is a self-contained system that incorporateselements of control logic and real-world interaction. UML State Ma-chines constitute a powerful formalism to model the behaviour ofthese types of systems. In current industrial environments, the soft-ware of these embedded systems have to cope with the increasingcomplexity and robustness requirements at runtime. One way tomanage these requirements is having the software component’sbehaviour model available at runtime ([email protected]). Thus,it is possible to enhance the safety of the software component byenabling verification and adaptation at runtime. In this paper, wepresent a model-driven approach to generate software components(namely, RESCO framework), which are able both to provide theirinternal information in model terms at runtime and adapt their be-haviour automatically when an error or an unexpected situation isdetected. The aforementioned runtime introspection and adaptationabilities are added automatically to the software component and itdoes not require the developer make any extra effort. The solutionhas been tested in the design and implementation of an industrialBurner controller. Results indicate that the software components ge-nerated by the presented solution provides introspection at runtime.Thanks to this introspection ability at runtime, the software com-ponents are able to adapt automatically from their normal-modebehaviour to a safe-mode behaviour which was defined to be usedin erroneous or unexpected situations at runtime. Therefore, it ispossible to enhance the safety of the systems consisting of thesesoftware components

MDE based IoT Service to enhance the safety of controllers at runtime

One of the challenges for complex IoT software systems is toincrease their safety. A Model Driven Development approach helps in the design and development phase of these systems while runtime checkin gtechniques help to enhance safety. To supervise the status of different IoT services that are registered in a local cloud at runtime, the solution that is presented in this work uses the information that it receives from the different services registered in a local cloud in model terms. The runtime checker, the new Safety related service of the Arrowhead framework, has predefined contracts to ensure the correctness of the services at runtime.Based on these contracts and checking the information that it receives at runtime it is able to detect unsafe scenarios. Once an unsafe scenario is detected, it starts a safe process to protect the behaviour of the whole system adapting the wrong service or services to a degraded operation mode at runtime. All these services will be Arrowhead compliant

Implementation of a holistic digital twin solution for design prototyping and virtual commissioning

Abstract Industry 4.0 has ushered in a new era of digital manufacturing and in this context, digital twins are considered as the next wave of simulation technologies. The development and commissioning of Cyber Physical Systems (CPS) is taking advantage of these technologies to improve product quality while reducing costs and time to market. However, existing practices of virtual design prototyping and commissioning require the cooperation of domain specific engineering fields. This involves considerable effort as development is mostly carried out in different departments using vendor specific simulation tools. There is still no integrated simulation environment commercially available, in which all engineering disciplines can work collaboratively. This presents a major challenge when interlinking virtual models with their physical counterparts. This paper therefore addresses these challenges by implementing a holistic and vendor agnostic digital twin solution for design prototyping and commissioning practices. The solution was tested in an industrial use case, in which the digital twin effectively prototyped cost‐efficient solar assembly lines

PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems

Current approaches to manufacturing must evolve to respond to increasing demands for short product life cycles and customised products. Adaptive manufacturing systems integrate advanced technologies, automation, and data-driven methodologies to develop adaptable, efficient, and responsive production processes. Central to this concept is the emphasis on human involvement and fostering synergy between human operators and the manufacturing system. Significant changes to the system's controller are required to achieve adaptivity, with programmable logic controllers (PLCs) being a common controller type. After the necessary changes to the configuration of the manufacturing system, the PLC should be reconfigured to orchestrate the new required behaviour. Automated reconfiguration is vital to rapidly responding to change, but some changes cannot be entirely achieved without human input in collaboration with automated methods. Conventional practices in PLC programming include manual, repetitive coding practices subject to errors. As a result, to ensure operational safety, the changes must be tested before being deployed to operations, ensuring it is error-free. This paper presents a methodology to automatically reconfigure the simulation environment and controller in response to a new product request. We automate the PLC code generation and testing practices to support and free up the operators when performing repetitive manufacturing reconfiguration tasks. The methodology is based on human learning, software automation, customised program development, knowledge graphs, and Graph Neural Networks (GNNs). The presented solution is a generic, vendor-agnostic, and interoperable solution that facilitates information exchange among multiple heterogeneous environments. Lastly, we have validated the methodology as a proof of concept at an adaptive assembly cell at the University of Nottingham in the United Kingdom